
@article{https://doi.org/10.1111/lnc3.12432,
	title = {Five sources of bias in natural language processing},
	volume = {15},
	url = {https://compass.onlinelibrary.wiley.com/doi/abs/10.1111/lnc3.12432},
	doi = {https://doi.org/10.1111/lnc3.12432},
	abstract = {Abstract Recently, there has been an increased interest in demographically grounded bias in natural language processing (NLP) applications. Much of the recent work has focused on describing bias and providing an overview of bias in a larger context. Here, we provide a simple, actionable summary of this recent work. We outline five sources where bias can occur in NLP systems: (1) the data, (2) the annotation process, (3) the input representations, (4) the models, and finally (5) the research design (or how we conceptualize our research). We explore each of the bias sources in detail in this article, including examples and links to related work, as well as potential counter-measures.},
	number = {8},
	journal = {Language and Linguistics Compass},
	author = {Hovy, Dirk and Prabhumoye, Shrimai},
	year = {2021},
	pages = {e12432},
}

@article{perrigo2023openai,
	title = {{OpenAI} used kenyan workers on less than \$2 per hour to make {ChatGPT} less toxic},
	url = {https://time.com/6247678/openai-chatgpt-kenya-workers/},
	journal = {TIME},
	author = {Perrigo, Billy},
	month = jan,
	year = {2023},
}

@misc{noauthor_openai_nodate,
	title = {{OpenAI} {Used} {Kenyan} {Workers} on {Less} {Than} \$2 {Per} {Hour}: {Exclusive} {\textbar} {TIME}},
	url = {https://time.com/6247678/openai-chatgpt-kenya-workers/},
	urldate = {2026-02-10},
}

@misc{olmo_olmo_2025,
	title = {Olmo 3},
	url = {https://arxiv.org/abs/2512.13961v1},
	abstract = {We introduce Olmo 3, a family of state-of-the-art, fully-open language models at the 7B and 32B parameter scales. Olmo 3 model construction targets long-context reasoning, function calling, coding, instruction following, general chat, and knowledge recall. This release includes the entire model flow, i.e., the full lifecycle of the family of models, including every stage, checkpoint, data point, and dependency used to build it. Our flagship model, Olmo 3 Think 32B, is the strongest fully-open thinking model released to-date.},
	language = {en},
	urldate = {2026-02-10},
	journal = {arXiv.org},
	author = {Olmo, Team and Ettinger, Allyson and Bertsch, Amanda and Kuehl, Bailey and Graham, David and Heineman, David and Groeneveld, Dirk and Brahman, Faeze and Timbers, Finbarr and Ivison, Hamish and Morrison, Jacob and Poznanski, Jake and Lo, Kyle and Soldaini, Luca and Jordan, Matt and Chen, Mayee and Noukhovitch, Michael and Lambert, Nathan and Walsh, Pete and Dasigi, Pradeep and Berry, Robert and Malik, Saumya and Shah, Saurabh and Geng, Scott and Arora, Shane and Gupta, Shashank and Anderson, Taira and Xiao, Teng and Murray, Tyler and Romero, Tyler and Graf, Victoria and Asai, Akari and Bhagia, Akshita and Wettig, Alexander and Liu, Alisa and Rangapur, Aman and Anastasiades, Chloe and Huang, Costa and Schwenk, Dustin and Trivedi, Harsh and Magnusson, Ian and Lochner, Jaron and Liu, Jiacheng and Miranda, Lester James V. and Sap, Maarten and Morgan, Malia and Schmitz, Michael and Guerquin, Michal and Wilson, Michael and Huff, Regan and Bras, Ronan Le and Xin, Rui and Shao, Rulin and Skjonsberg, Sam and Shen, Shannon Zejiang and Li, Shuyue Stella and Wilde, Tucker and Pyatkin, Valentina and Merrill, Will and Chang, Yapei and Gu, Yuling and Zeng, Zhiyuan and Sabharwal, Ashish and Zettlemoyer, Luke and Koh, Pang Wei and Farhadi, Ali and Smith, Noah A. and Hajishirzi, Hannaneh},
	month = dec,
	year = {2025},
}

@misc{casper_open_2023,
	title = {Open {Problems} and {Fundamental} {Limitations} of {Reinforcement} {Learning} from {Human} {Feedback}},
	url = {http://arxiv.org/abs/2307.15217},
	doi = {10.48550/arXiv.2307.15217},
	abstract = {Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals. RLHF has emerged as the central method used to finetune state-of-the-art large language models (LLMs). Despite this popularity, there has been relatively little public work systematizing its flaws. In this paper, we (1) survey open problems and fundamental limitations of RLHF and related methods; (2) overview techniques to understand, improve, and complement RLHF in practice; and (3) propose auditing and disclosure standards to improve societal oversight of RLHF systems. Our work emphasizes the limitations of RLHF and highlights the importance of a multi-faceted approach to the development of safer AI systems.},
	urldate = {2026-02-10},
	publisher = {arXiv},
	author = {Casper, Stephen and Davies, Xander and Shi, Claudia and Gilbert, Thomas Krendl and Scheurer, Jérémy and Rando, Javier and Freedman, Rachel and Korbak, Tomasz and Lindner, David and Freire, Pedro and Wang, Tony and Marks, Samuel and Segerie, Charbel-Raphaël and Carroll, Micah and Peng, Andi and Christoffersen, Phillip and Damani, Mehul and Slocum, Stewart and Anwar, Usman and Siththaranjan, Anand and Nadeau, Max and Michaud, Eric J. and Pfau, Jacob and Krasheninnikov, Dmitrii and Chen, Xin and Langosco, Lauro and Hase, Peter and Bıyık, Erdem and Dragan, Anca and Krueger, David and Sadigh, Dorsa and Hadfield-Menell, Dylan},
	month = sep,
	year = {2023},
	note = {arXiv:2307.15217 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@inproceedings{hovy_social_2016,
	address = {Berlin, Germany},
	title = {The {Social} {Impact} of {Natural} {Language} {Processing}},
	url = {https://aclanthology.org/P16-2096/},
	doi = {10.18653/v1/P16-2096},
	urldate = {2026-02-10},
	booktitle = {Proceedings of the 54th {Annual} {Meeting} of the {Association} for {Computational} {Linguistics} ({Volume} 2: {Short} {Papers})},
	publisher = {Association for Computational Linguistics},
	author = {Hovy, Dirk and Spruit, Shannon L.},
	editor = {Erk, Katrin and Smith, Noah A.},
	month = aug,
	year = {2016},
	pages = {591--598},
}

@inproceedings{sap_risk_2019,
	address = {Florence, Italy},
	title = {The {Risk} of {Racial} {Bias} in {Hate} {Speech} {Detection}},
	url = {https://aclanthology.org/P19-1163/},
	doi = {10.18653/v1/P19-1163},
	abstract = {We investigate how annotators' insensitivity to differences in dialect can lead to racial bias in automatic hate speech detection models, potentially amplifying harm against minority populations. We first uncover unexpected correlations between surface markers of African American English (AAE) and ratings of toxicity in several widely-used hate speech datasets. Then, we show that models trained on these corpora acquire and propagate these biases, such that AAE tweets and tweets by self-identified African Americans are up to two times more likely to be labelled as offensive compared to others. Finally, we propose *dialect* and *race priming* as ways to reduce the racial bias in annotation, showing that when annotators are made explicitly aware of an AAE tweet's dialect they are significantly less likely to label the tweet as offensive.},
	urldate = {2026-02-10},
	booktitle = {Proceedings of the 57th {Annual} {Meeting} of the {Association} for {Computational} {Linguistics}},
	publisher = {Association for Computational Linguistics},
	author = {Sap, Maarten and Card, Dallas and Gabriel, Saadia and Choi, Yejin and Smith, Noah A.},
	editor = {Korhonen, Anna and Traum, David and Màrquez, Lluís},
	month = jul,
	year = {2019},
	pages = {1668--1678},
}

@article{liang_gpt_2023,
	title = {{GPT} detectors are biased against non-native {English} writers},
	volume = {4},
	issn = {2666-3899},
	url = {https://www.sciencedirect.com/science/article/pii/S2666389923001307},
	doi = {10.1016/j.patter.2023.100779},
	abstract = {GPT detectors frequently misclassify non-native English writing as AI generated, raising concerns about fairness and robustness. Addressing the biases in these detectors is crucial to prevent the marginalization of non-native English speakers in evaluative and educational settings and to create a more equitable digital landscape.},
	number = {7},
	urldate = {2026-02-10},
	journal = {Patterns},
	author = {Liang, Weixin and Yuksekgonul, Mert and Mao, Yining and Wu, Eric and Zou, James},
	month = jul,
	year = {2023},
	pages = {100779},
}

@article{ebibi_study_2018,
	title = {A study on the use of contractions in speech and writing by selected students of the {University} of {Jos}},
	volume = {7},
	issn = {2227-5460, 2225-8604},
	url = {https://www.ajol.info/index.php/laligens/article/view/169917},
	doi = {10.4314/laligens.v7i1.9},
	abstract = {The study examined the impact (of the wrong use) contraction on speech and writing of Remedial Sciences students of the University of Jos. The study specifically sought to find out their knowledge of the contracted forms of verbs, how and in what contexts students use the contracted forms in writing and speech and whether the contractions have any impact on their writing and speaking skills. For the purpose of data collection, questionnaires were administered to 300 Remedial Sciences students selected from a class of 700 students.},
	language = {en},
	number = {1},
	urldate = {2025-12-14},
	journal = {AFRREV LALIGENS: An International Journal of Language, Literature and Gender Studies},
	author = {Ebibi, Johnson Ojeka and Aaron, Vera Anap and Rejoice-James, Songden and Adeyemo, Ololade},
	month = apr,
	year = {2018},
	pages = {82},
}

@article{christiana-oluremi_lexico-grammatical_2013,
	title = {Lexico-{Grammatical} {Features} of {Nigerian} {English}},
	volume = {1},
	issn = {2325-0887},
	url = {http://www.macrothink.org/journal/index.php/ijele/article/view/3316},
	doi = {10.5296/ijele.v1i1.3316},
	abstract = {English is a second language which serves a number of purposes in Nigeria and certain socio-cultural variables influence its use in the Nigerian context. There is rapid expansion in the vocabulary used by Nigerians to translate their thought into language. This has brought about different coinages to suit the situation and context of usage. The English language lacks the ability to perfectly capture all aspects of Nigerian culture, hence it is frequently augmented by fresh coinages and importations derived from Nigerian English lexis as different from native speakers’ lexis. There is the introduction of culture-specific vocabulary items into the English language by Nigerian speakers of the language. This has led to creativity which manifests in coinages to reflect the Nigerian experience. Examples of Nigerian English are drawn and examined in this paper to reveal the influx of new words into the English language as occasioned by lexical derivations and innovations in the Nigerian usage of the language.},
	language = {en},
	number = {1},
	urldate = {2025-12-14},
	journal = {International Journal of English Language Education},
	author = {Christiana-Oluremi, Ajewole-Orimogunje},
	month = feb,
	year = {2013},
	pages = {pp. 261--271},
}

@article{akintayo_study_2024,
	title = {A {STUDY} {OF} {FORMAL} {AND} {INFORMAL} {USES} {OF} {ENGLISH} {IN} {NIGERIA}},
	volume = {8},
	issn = {2395-5228, 2395-521X},
	url = {https://iaeme.com/MasterAdmin/Journal_uploads/IJARSSH/VOLUME_8_ISSUE_2/IJARSSH_08_02_009.pdf},
	doi = {10.34218/IJARSSH_08_02_009},
	abstract = {An important aspect of the use of any language is for the users to know when to apply the rules of formal and informal usages. It is crucial that the user’s choices of sounds, words, phrases, sentences and texts are meaningful. As a form of differentiation, the formal texts admit of the rules of standardisation while informal ones do not comply with the rules of standardisation. Both formal and informal uses must, nevertheless, comply with the rules of contextual configurations of their meanings. This paper is a study of sounds, words, phrases, sentences and texts/discourse that are both formal and informal in English in order to determine whether the users can draw the line between them. Naturally-occurring texts both in spoken and written forms collected from several sources form the data and the concepts of Ideational and Interpersonal Metafunctions grounded in the Hallidayan Systemic Functional Theory are used for the analysis. Their meanings are provided through the paradigmatic and syntagmatic networks of meaning potential. The findings show that formal and informal usages are not distinctively delineated by many Nigerian users of English in their spoken and written expressions. The study recommends that users of English should always consider appropriate choices and not blur the lines between formal and informal usages both in spoken and written discourses. This study will extend the perspective on formal and informal uses of English in Nigeria.},
	language = {en},
	number = {2},
	urldate = {2025-12-14},
	journal = {INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN SOCIAL SCIENCES AND HUMANITIES},
	author = {Akintayo, Abiodun and Daramola, Adeyemi},
	month = dec,
	year = {2024},
	pages = {108--120},
}

@article{kperogi_divided_2010,
	title = {Divided by a {Common} {Language}: {A} {Comparison} of {Nigerian}, {American} and {British} {English}},
	shorttitle = {Divided by a {Common} {Language}},
	url = {https://www.academia.edu/57526431/Divided_by_a_Common_Language_A_Comparison_of_Nigerian_American_and_British_English},
	abstract = {We all know that there is such a thing as British English; it is the progenitor of all subsequent \&quot;Englishes\&quot; (as professional linguists awkwardly call national and sub-regional varieties of the English language) in the world. And we do, of},
	urldate = {2025-12-14},
	author = {Kperogi, Farooq},
	month = jan,
	year = {2010},
}

@misc{kearney_language_2025,
	title = {Language {Models} {Change} {Facts} {Based} on the {Way} {You} {Talk}},
	url = {http://arxiv.org/abs/2507.14238},
	doi = {10.48550/arXiv.2507.14238},
	abstract = {Large language models (LLMs) are increasingly being used in user-facing applications, from providing medical consultations to job interview advice. Recent research suggests that these models are becoming increasingly proficient at inferring identity information about the author of a piece of text from linguistic patterns as subtle as the choice of a few words. However, little is known about how LLMs use this information in their decision-making in real-world applications. We perform the first comprehensive analysis of how identity markers present in a user's writing bias LLM responses across five different high-stakes LLM applications in the domains of medicine, law, politics, government benefits, and job salaries. We find that LLMs are extremely sensitive to markers of identity in user queries and that race, gender, and age consistently influence LLM responses in these applications. For instance, when providing medical advice, we find that models apply different standards of care to individuals of different ethnicities for the same symptoms; we find that LLMs are more likely to alter answers to align with a conservative (liberal) political worldview when asked factual questions by older (younger) individuals; and that LLMs recommend lower salaries for non-White job applicants and higher salaries for women compared to men. Taken together, these biases mean that the use of off-the-shelf LLMs for these applications may cause harmful differences in medical care, foster wage gaps, and create different political factual realities for people of different identities. Beyond providing an analysis, we also provide new tools for evaluating how subtle encoding of identity in users' language choices impacts model decisions. Given the serious implications of these findings, we recommend that similar thorough assessments of LLM use in user-facing applications are conducted before future deployment.},
	urldate = {2025-10-25},
	publisher = {arXiv},
	author = {Kearney, Matthew and Binns, Reuben and Gal, Yarin},
	month = jul,
	year = {2025},
	note = {arXiv:2507.14238 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Computers and Society},
}

@inproceedings{podesva_three_2008,
	title = {Three {Sources} of {Stylistic} {Meaning}},
	url = {https://asol.ling.utexas.edu/salsa/proceedings/2007/Podesva.pdf},
	publisher = {University of Texas at Austin, Department of Linguistics},
	author = {Podesva, Robert J.},
	year = {2008},
	pages = {134--143},
}

@incollection{eckert_communities_1992,
	address = {Berkeley},
	title = {Communities of {Practice}: {Where} {Language}, {Gender}, and {Power} {All} {Live}},
	booktitle = {Locating {Power}: {Proceedings} of the 1992 {Berkeley} {Women} and {Language} {Conference}},
	publisher = {Berkeley Women and Language Group},
	author = {Eckert, Penelope and McConnell-Ginet, Sally},
	editor = {Hall, Kira and Bucholtz, Mary and Moonwomon, Birch},
	year = {1992},
	pages = {89--99},
}

@book{lippi-green_english_2012,
	address = {London},
	edition = {2},
	title = {English with an {Accent}: {Language}, {Ideology} and {Discrimination} in the {United} {States}},
	isbn = {978-0-415-55911-9},
	doi = {10.4324/9780203348802},
	publisher = {Routledge},
	author = {Lippi-Green, Rosina},
	year = {2012},
}

@article{radford_improving_2024,
	title = {Improving {Language} {Understanding} by {Generative} {Pre}-{Training}},
	abstract = {Natural language understanding comprises a wide range of diverse tasks such as textual entailment, question answering, semantic similarity assessment, and document classiﬁcation. Although large unlabeled text corpora are abundant, labeled data for learning these speciﬁc tasks is scarce, making it challenging for discriminatively trained models to perform adequately. We demonstrate that large gains on these tasks can be realized by generative pre-training of a language model on a diverse corpus of unlabeled text, followed by discriminative ﬁne-tuning on each speciﬁc task. In contrast to previous approaches, we make use of task-aware input transformations during ﬁne-tuning to achieve effective transfer while requiring minimal changes to the model architecture. We demonstrate the effectiveness of our approach on a wide range of benchmarks for natural language understanding. Our general task-agnostic model outperforms discriminatively trained models that use architectures speciﬁcally crafted for each task, signiﬁcantly improving upon the state of the art in 9 out of the 12 tasks studied. For instance, we achieve absolute improvements of 8.9\% on commonsense reasoning (Stories Cloze Test), 5.7\% on question answering (RACE), and 1.5\% on textual entailment (MultiNLI).},
	language = {en},
	author = {Radford, Alec and Narasimhan, Karthik and Salimans, Tim and Sutskever, Ilya},
	month = feb,
	year = {2024},
}

@misc{noauthor_improving_2024,
	title = {Improving language understanding with unsupervised learning},
	url = {https://openai.com/index/language-unsupervised/},
	abstract = {We’ve obtained state-of-the-art results on a suite of diverse language tasks with a scalable, task-agnostic system, which we’re also releasing. Our approach is a combination of two existing ideas: transformers and unsupervised pre-training. These results provide a convincing example that pairing supervised learning methods with unsupervised pre-training works very well; this is an idea that many have explored in the past, and we hope our result motivates further research into applying this idea on larger and more diverse datasets.},
	language = {en-US},
	urldate = {2025-10-19},
	month = feb,
	year = {2024},
}

@inproceedings{mitchell_detectgpt_2023,
	title = {{DetectGPT}: {Zero}-{Shot} {Machine}-{Generated} {Text} {Detection} using {Probability} {Curvature}},
	issn = {2640-3498},
	shorttitle = {{DetectGPT}},
	url = {https://proceedings.mlr.press/v202/mitchell23a.html},
	abstract = {The increasing fluency and widespread usage of large language models (LLMs) highlight the desirability of corresponding tools aiding detection of LLM-generated text. In this paper, we identify a property of the structure of an LLM’s probability function that is useful for such detection. Specifically, we demonstrate that text sampled from an LLM tends to occupy negative curvature regions of the model’s log probability function. Leveraging this observation, we then define a new curvature-based criterion for judging if a passage is generated from a given LLM. This approach, which we call DetectGPT, does not require training a separate classifier, collecting a dataset of real or generated passages, or explicitly watermarking generated text. It uses only log probabilities computed by the model of interest and random perturbations of the passage from another generic pre-trained language model (e.g., T5). We find DetectGPT is more discriminative than existing zero-shot methods for model sample detection, notably improving detection of fake news articles generated by 20B parameter GPT-NeoX from 0.81 AUROC for the strongest zero-shot baseline to 0.95 AUROC for DetectGPT.},
	language = {en},
	urldate = {2025-10-19},
	booktitle = {Proceedings of the 40th {International} {Conference} on {Machine} {Learning}},
	publisher = {PMLR},
	author = {Mitchell, Eric and Lee, Yoonho and Khazatsky, Alexander and Manning, Christopher D. and Finn, Chelsea},
	month = jul,
	year = {2023},
	pages = {24950--24962},
}

@misc{wang_whose_2022,
	title = {Whose {AI} {Dream}? {In} search of the aspiration in data annotation},
	shorttitle = {Whose {AI} {Dream}?},
	url = {http://arxiv.org/abs/2203.10748},
	doi = {10.48550/arXiv.2203.10748},
	abstract = {This paper present the practice of data annotation from the perspective of the annotators. Data is fundamental to ML models. This paper investigates the work practices concerning data annotation as performed in the industry, in India. Previous investigations have largely focused on annotator subjectivity, bias and efficiency. We present a wider perspective of the data annotation, following a grounded approach, we conducted three sets of interviews with 25 annotators, 10 industry experts and 12 ML practitioners. Our results show that the work of annotators is dictated by the interests, priorities and values of others above their station. More than technical, we contend that data annotation is a systematic exercise of power through organizational structure and practice. We propose a set of implications for how we can cultivate and encourage better practice to balance the tension between the need for high quality data at low cost and the annotator aspiration for well being, career perspective, and active participation in building the AI dream.},
	urldate = {2025-10-19},
	publisher = {arXiv},
	author = {Wang, Ding and Prabhat, Shantanu and Sambasivan, Nithya},
	month = mar,
	year = {2022},
	note = {arXiv:2203.10748 [cs]},
	keywords = {Computer Science - Computers and Society, Computer Science - Human-Computer Interaction},
}

@misc{ouyang_training_2022,
	title = {Training language models to follow instructions with human feedback},
	url = {http://arxiv.org/abs/2203.02155},
	doi = {10.48550/arXiv.2203.02155},
	abstract = {Making language models bigger does not inherently make them better at following a user's intent. For example, large language models can generate outputs that are untruthful, toxic, or simply not helpful to the user. In other words, these models are not aligned with their users. In this paper, we show an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning with human feedback. Starting with a set of labeler-written prompts and prompts submitted through the OpenAI API, we collect a dataset of labeler demonstrations of the desired model behavior, which we use to fine-tune GPT-3 using supervised learning. We then collect a dataset of rankings of model outputs, which we use to further fine-tune this supervised model using reinforcement learning from human feedback. We call the resulting models InstructGPT. In human evaluations on our prompt distribution, outputs from the 1.3B parameter InstructGPT model are preferred to outputs from the 175B GPT-3, despite having 100x fewer parameters. Moreover, InstructGPT models show improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets. Even though InstructGPT still makes simple mistakes, our results show that fine-tuning with human feedback is a promising direction for aligning language models with human intent.},
	urldate = {2025-10-19},
	publisher = {arXiv},
	author = {Ouyang, Long and Wu, Jeff and Jiang, Xu and Almeida, Diogo and Wainwright, Carroll L. and Mishkin, Pamela and Zhang, Chong and Agarwal, Sandhini and Slama, Katarina and Ray, Alex and Schulman, John and Hilton, Jacob and Kelton, Fraser and Miller, Luke and Simens, Maddie and Askell, Amanda and Welinder, Peter and Christiano, Paul and Leike, Jan and Lowe, Ryan},
	month = mar,
	year = {2022},
	note = {arXiv:2203.02155 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@article{grieve_sociolinguistic_2025,
	title = {The sociolinguistic foundations of language modeling},
	volume = {7},
	issn = {2624-8212},
	url = {https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2024.1472411/full},
	doi = {10.3389/frai.2024.1472411},
	abstract = {In this article, we introduce a sociolinguistic perspective on language modeling. We claim that language models in general are inherently modeling varieties of language, and we consider how this insight can inform the development and deployment of language models. We begin by presenting a technical definition of the concept of a variety of language as developed in sociolinguistics. We then discuss how this perspective could help us better understand five basic challenges in language modeling: social bias, domain adaptation, alignment, language change, and scale. We argue that to maximize the performance and societal value of language models it is important to carefully compile training corpora that accurately represent the specific varieties of language being modeled, drawing on theories, methods, and descriptions from the field of sociolinguistics.},
	language = {English},
	urldate = {2025-10-19},
	journal = {Frontiers in Artificial Intelligence},
	publisher = {Frontiers},
	author = {Grieve, Jack and Bartl, Sara and Fuoli, Matteo and Grafmiller, Jason and Huang, Weihang and Jawerbaum, Alejandro and Murakami, Akira and Perlman, Marcus and Roemling, Dana and Winter, Bodo},
	month = jan,
	year = {2025},
	keywords = {AI ethics, artificial intelligence, computational sociolinguistics, corpus linguistics, large language models, natural language processing, varieties of language},
}

@misc{juzek_why_2024,
	title = {Why {Does} {ChatGPT} "{Delve}" {So} {Much}? {Exploring} the {Sources} of {Lexical} {Overrepresentation} in {Large} {Language} {Models}},
	shorttitle = {Why {Does} {ChatGPT} "{Delve}" {So} {Much}?},
	url = {http://arxiv.org/abs/2412.11385},
	doi = {10.48550/arXiv.2412.11385},
	abstract = {Scientific English is currently undergoing rapid change, with words like "delve," "intricate," and "underscore" appearing far more frequently than just a few years ago. It is widely assumed that scientists' use of large language models (LLMs) is responsible for such trends. We develop a formal, transferable method to characterize these linguistic changes. Application of our method yields 21 focal words whose increased occurrence in scientific abstracts is likely the result of LLM usage. We then pose "the puzzle of lexical overrepresentation": WHY are such words overused by LLMs? We fail to find evidence that lexical overrepresentation is caused by model architecture, algorithm choices, or training data. To assess whether reinforcement learning from human feedback (RLHF) contributes to the overuse of focal words, we undertake comparative model testing and conduct an exploratory online study. While the model testing is consistent with RLHF playing a role, our experimental results suggest that participants may be reacting differently to "delve" than to other focal words. With LLMs quickly becoming a driver of global language change, investigating these potential sources of lexical overrepresentation is important. We note that while insights into the workings of LLMs are within reach, a lack of transparency surrounding model development remains an obstacle to such research.},
	urldate = {2025-10-19},
	publisher = {arXiv},
	author = {Juzek, Tom S. and Ward, Zina B.},
	month = dec,
	year = {2024},
	note = {arXiv:2412.11385 [cs]
version: 1},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@misc{ice_project_university_of_zurich__de_la_salle_university_manila_ice-philippines_2004,
	title = {{ICE}-{Philippines} ({International} {Corpus} of {English})},
	url = {https://www.ice-corpora.uzh.ch/en/joinice/Teams/icephi.html},
	author = {{ICE Project, University of Zürich \& De La Salle University, Manila}},
	year = {2004},
}

@misc{ice_project_university_of_zurich__chemnitz_university_of_technology__university_of_bayreuth_ice-east_1999,
	title = {{ICE}-{East} {Africa} ({International} {Corpus} of {English}): {Kenya} \& {Tanzania} component},
	url = {https://www.ice-corpora.uzh.ch/en/joinice/Teams/iceea.html},
	author = {{ICE Project, University of Zürich \& Chemnitz University of Technology / University of Bayreuth}},
	year = {1999},
}

@misc{victoria_university_of_wellington_school_of_linguistics__applied_language_studies__university_of_zurich_ice_project_ice-new_1999,
	title = {{ICE}-{New} {Zealand} ({International} {Corpus} of {English})},
	url = {https://www.ice-corpora.uzh.ch/en/joinice/Teams/icenz.html},
	author = {{Victoria University of Wellington, School of Linguistics \& Applied Language Studies / University of Zürich, ICE Project}},
	year = {1999},
}

@article{wu_survey_2025,
	title = {A {Survey} on {LLM}-{Generated} {Text} {Detection}: {Necessity}, {Methods}, and {Future} {Directions}},
	volume = {51},
	url = {https://direct.mit.edu/coli/article/51/1/275/127462/A-Survey-on-LLM-Generated-Text-Detection-Necessity},
	doi = {10.1162/coli_a_00549},
	number = {1},
	journal = {Computational Linguistics},
	publisher = {MIT Press},
	author = {Wu, Junchao and Yang, Shu and Zhan, Runzhe and Yuan, Yulin and Wong, Derek Fai and Chao, Lidia Sam},
	year = {2025},
	pages = {275--317},
}

@article{godoy_metas_2025,
	chapter = {Boards, Policy \& Regulation},
	title = {Meta's \$14.8 billion {Scale} {AI} deal latest test of {AI} partnerships},
	url = {https://www.reuters.com/sustainability/boards-policy-regulation/metas-148-billion-scale-ai-deal-latest-test-ai-partnerships-2025-06-13/},
	abstract = {Facebook owner Meta's \$14.8 billion investment in Scale AI and hiring of the data-labeling startup's CEO will test how the Trump administration views so-called acquihire deals, which some have criticized as an attempt to evade regulatory scrutiny.},
	language = {en},
	urldate = {2025-10-19},
	journal = {Reuters},
	author = {Godoy, Jody and Godoy, Jody},
	month = jun,
	year = {2025},
}

@misc{gut_international_2014,
	title = {International {Corpus} of {English} – {Nigeria} ({ICE}-{Nigeria})},
	url = {https://sourceforge.net/projects/ice-nigeria/},
	author = {Gut, Ulrike and Antia, Bassey E. and Anietie, Okon and Simo Bobda, Augustin and Onukawa, Michael and {others}},
	year = {2014},
}

@misc{davies_corpus_2008,
	title = {The {Corpus} of {Contemporary} {American} {English} ({COCA})},
	url = {https://www.english-corpora.org/coca/},
	author = {Davies, Mark},
	year = {2008},
}

@misc{li_alpacaeval_2023,
	title = {{AlpacaEval}: {An} {Automatic} {Evaluator} of {Instruction}-following {Models}},
	url = {https://github.com/tatsu-lab/alpaca_eval},
	publisher = {GitHub},
	author = {Li, Xuechen and Zhang, Tianyi and Dubois, Yann and Taori, Rohan and Gulrajani, Ishaan and Guestrin, Carlos and Liang, Percy and Hashimoto, Tatsunori B.},
	month = may,
	year = {2023},
	note = {Publication Title: GitHub repository},
}

@misc{zheng_judging_2023,
	title = {Judging {LLM}-as-a-{Judge} with {MT}-{Bench} and {Chatbot} {Arena}},
	url = {http://arxiv.org/abs/2306.05685},
	doi = {10.48550/arXiv.2306.05685},
	abstract = {Evaluating large language model (LLM) based chat assistants is challenging due to their broad capabilities and the inadequacy of existing benchmarks in measuring human preferences. To address this, we explore using strong LLMs as judges to evaluate these models on more open-ended questions. We examine the usage and limitations of LLM-as-a-judge, including position, verbosity, and self-enhancement biases, as well as limited reasoning ability, and propose solutions to mitigate some of them. We then verify the agreement between LLM judges and human preferences by introducing two benchmarks: MT-bench, a multi-turn question set; and Chatbot Arena, a crowdsourced battle platform. Our results reveal that strong LLM judges like GPT-4 can match both controlled and crowdsourced human preferences well, achieving over 80\% agreement, the same level of agreement between humans. Hence, LLM-as-a-judge is a scalable and explainable way to approximate human preferences, which are otherwise very expensive to obtain. Additionally, we show our benchmark and traditional benchmarks complement each other by evaluating several variants of LLaMA and Vicuna. The MT-bench questions, 3K expert votes, and 30K conversations with human preferences are publicly available at https://github.com/lm-sys/FastChat/tree/main/fastchat/llm\_judge.},
	urldate = {2025-10-19},
	publisher = {arXiv},
	author = {Zheng, Lianmin and Chiang, Wei-Lin and Sheng, Ying and Zhuang, Siyuan and Wu, Zhanghao and Zhuang, Yonghao and Lin, Zi and Li, Zhuohan and Li, Dacheng and Xing, Eric P. and Zhang, Hao and Gonzalez, Joseph E. and Stoica, Ion},
	month = dec,
	year = {2023},
	note = {arXiv:2306.05685 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language},
}

@article{lin_divergence_1991,
	title = {Divergence measures based on the {Shannon} entropy},
	volume = {37},
	issn = {1557-9654},
	url = {https://ieeexplore.ieee.org/document/61115},
	doi = {10.1109/18.61115},
	abstract = {A novel class of information-theoretic divergence measures based on the Shannon entropy is introduced. Unlike the well-known Kullback divergences, the new measures do not require the condition of absolute continuity to be satisfied by the probability distributions involved. More importantly, their close relationship with the variational distance and the probability of misclassification error are established in terms of bounds. These bounds are crucial in many applications of divergence measures. The measures are also well characterized by the properties of nonnegativity, finiteness, semiboundedness, and boundedness.{\textless}{\textgreater}},
	number = {1},
	urldate = {2025-10-19},
	journal = {IEEE Transactions on Information Theory},
	author = {Lin, J.},
	month = jan,
	year = {1991},
	keywords = {Computer science, Entropy, Genetics, Pattern analysis, Pattern recognition, Probability distribution, Signal analysis, Signal processing, Taxonomy, Upper bound},
	pages = {145--151},
}

@misc{grattafiori_llama_2024,
	title = {The {Llama} 3 {Herd} of {Models}},
	url = {http://arxiv.org/abs/2407.21783},
	doi = {10.48550/arXiv.2407.21783},
	abstract = {Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.},
	urldate = {2025-10-19},
	publisher = {arXiv},
	author = {Grattafiori, Aaron and Dubey, Abhimanyu and Jauhri, Abhinav and Pandey, Abhinav and Kadian, Abhishek and Al-Dahle, Ahmad and Letman, Aiesha and Mathur, Akhil and Schelten, Alan and Vaughan, Alex and Yang, Amy and Fan, Angela and Goyal, Anirudh and Hartshorn, Anthony and Yang, Aobo and Mitra, Archi and Sravankumar, Archie and Korenev, Artem and Hinsvark, Arthur and Rao, Arun and Zhang, Aston and Rodriguez, Aurelien and Gregerson, Austen and Spataru, Ava and Roziere, Baptiste and Biron, Bethany and Tang, Binh and Chern, Bobbie and Caucheteux, Charlotte and Nayak, Chaya and Bi, Chloe and Marra, Chris and McConnell, Chris and Keller, Christian and Touret, Christophe and Wu, Chunyang and Wong, Corinne and Ferrer, Cristian Canton and Nikolaidis, Cyrus and Allonsius, Damien and Song, Daniel and Pintz, Danielle and Livshits, Danny and Wyatt, Danny and Esiobu, David and Choudhary, Dhruv and Mahajan, Dhruv and Garcia-Olano, Diego and Perino, Diego and Hupkes, Dieuwke and Lakomkin, Egor and AlBadawy, Ehab and Lobanova, Elina and Dinan, Emily and Smith, Eric Michael and Radenovic, Filip and Guzmán, Francisco and Zhang, Frank and Synnaeve, Gabriel and Lee, Gabrielle and Anderson, Georgia Lewis and Thattai, Govind and Nail, Graeme and Mialon, Gregoire and Pang, Guan and Cucurell, Guillem and Nguyen, Hailey and Korevaar, Hannah and Xu, Hu and Touvron, Hugo and Zarov, Iliyan and Ibarra, Imanol Arrieta and Kloumann, Isabel and Misra, Ishan and Evtimov, Ivan and Zhang, Jack and Copet, Jade and Lee, Jaewon and Geffert, Jan and Vranes, Jana and Park, Jason and Mahadeokar, Jay and Shah, Jeet and Linde, Jelmer van der and Billock, Jennifer and Hong, Jenny and Lee, Jenya and Fu, Jeremy and Chi, Jianfeng and Huang, Jianyu and Liu, Jiawen and Wang, Jie and Yu, Jiecao and Bitton, Joanna and Spisak, Joe and Park, Jongsoo and Rocca, Joseph and Johnstun, Joshua and Saxe, Joshua and Jia, Junteng and Alwala, Kalyan Vasuden and Prasad, Karthik and Upasani, Kartikeya and Plawiak, Kate and Li, Ke and Heafield, Kenneth and Stone, Kevin and El-Arini, Khalid and Iyer, Krithika and Malik, Kshitiz and Chiu, Kuenley and Bhalla, Kunal and Lakhotia, Kushal and Rantala-Yeary, Lauren and Maaten, Laurens van der and Chen, Lawrence and Tan, Liang and Jenkins, Liz and Martin, Louis and Madaan, Lovish and Malo, Lubo and Blecher, Lukas and Landzaat, Lukas and Oliveira, Luke de and Muzzi, Madeline and Pasupuleti, Mahesh and Singh, Mannat and Paluri, Manohar and Kardas, Marcin and Tsimpoukelli, Maria and Oldham, Mathew and Rita, Mathieu and Pavlova, Maya and Kambadur, Melanie and Lewis, Mike and Si, Min and Singh, Mitesh Kumar and Hassan, Mona and Goyal, Naman and Torabi, Narjes and Bashlykov, Nikolay and Bogoychev, Nikolay and Chatterji, Niladri and Zhang, Ning and Duchenne, Olivier and Çelebi, Onur and Alrassy, Patrick and Zhang, Pengchuan and Li, Pengwei and Vasic, Petar and Weng, Peter and Bhargava, Prajjwal and Dubal, Pratik and Krishnan, Praveen and Koura, Punit Singh and Xu, Puxin and He, Qing and Dong, Qingxiao and Srinivasan, Ragavan and Ganapathy, Raj and Calderer, Ramon and Cabral, Ricardo Silveira and Stojnic, Robert and Raileanu, Roberta and Maheswari, Rohan and Girdhar, Rohit and Patel, Rohit and Sauvestre, Romain and Polidoro, Ronnie and Sumbaly, Roshan and Taylor, Ross and Silva, Ruan and Hou, Rui and Wang, Rui and Hosseini, Saghar and Chennabasappa, Sahana and Singh, Sanjay and Bell, Sean and Kim, Seohyun Sonia and Edunov, Sergey and Nie, Shaoliang and Narang, Sharan and Raparthy, Sharath and Shen, Sheng and Wan, Shengye and Bhosale, Shruti and Zhang, Shun and Vandenhende, Simon and Batra, Soumya and Whitman, Spencer and Sootla, Sten and Collot, Stephane and Gururangan, Suchin and Borodinsky, Sydney and Herman, Tamar and Fowler, Tara and Sheasha, Tarek and Georgiou, Thomas and Scialom, Thomas and Speckbacher, Tobias and Mihaylov, Todor and Xiao, Tong and Karn, Ujjwal and Goswami, Vedanuj and Gupta, Vibhor and Ramanathan, Vignesh and Kerkez, Viktor and Gonguet, Vincent and Do, Virginie and Vogeti, Vish and Albiero, Vítor and Petrovic, Vladan and Chu, Weiwei and Xiong, Wenhan and Fu, Wenyin and Meers, Whitney and Martinet, Xavier and Wang, Xiaodong and Wang, Xiaofang and Tan, Xiaoqing Ellen and Xia, Xide and Xie, Xinfeng and Jia, Xuchao and Wang, Xuewei and Goldschlag, Yaelle and Gaur, Yashesh and Babaei, Yasmine and Wen, Yi and Song, Yiwen and Zhang, Yuchen and Li, Yue and Mao, Yuning and Coudert, Zacharie Delpierre and Yan, Zheng and Chen, Zhengxing and Papakipos, Zoe and Singh, Aaditya and Srivastava, Aayushi and Jain, Abha and Kelsey, Adam and Shajnfeld, Adam and Gangidi, Adithya and Victoria, Adolfo and Goldstand, Ahuva and Menon, Ajay and Sharma, Ajay and Boesenberg, Alex and Baevski, Alexei and Feinstein, Allie and Kallet, Amanda and Sangani, Amit and Teo, Amos and Yunus, Anam and Lupu, Andrei and Alvarado, Andres and Caples, Andrew and Gu, Andrew and Ho, Andrew and Poulton, Andrew and Ryan, Andrew and Ramchandani, Ankit and Dong, Annie and Franco, Annie and Goyal, Anuj and Saraf, Aparajita and Chowdhury, Arkabandhu and Gabriel, Ashley and Bharambe, Ashwin and Eisenman, Assaf and Yazdan, Azadeh and James, Beau and Maurer, Ben and Leonhardi, Benjamin and Huang, Bernie and Loyd, Beth and Paola, Beto De and Paranjape, Bhargavi and Liu, Bing and Wu, Bo and Ni, Boyu and Hancock, Braden and Wasti, Bram and Spence, Brandon and Stojkovic, Brani and Gamido, Brian and Montalvo, Britt and Parker, Carl and Burton, Carly and Mejia, Catalina and Liu, Ce and Wang, Changhan and Kim, Changkyu and Zhou, Chao and Hu, Chester and Chu, Ching-Hsiang and Cai, Chris and Tindal, Chris and Feichtenhofer, Christoph and Gao, Cynthia and Civin, Damon and Beaty, Dana and Kreymer, Daniel and Li, Daniel and Adkins, David and Xu, David and Testuggine, Davide and David, Delia and Parikh, Devi and Liskovich, Diana and Foss, Didem and Wang, Dingkang and Le, Duc and Holland, Dustin and Dowling, Edward and Jamil, Eissa and Montgomery, Elaine and Presani, Eleonora and Hahn, Emily and Wood, Emily and Le, Eric-Tuan and Brinkman, Erik and Arcaute, Esteban and Dunbar, Evan and Smothers, Evan and Sun, Fei and Kreuk, Felix and Tian, Feng and Kokkinos, Filippos and Ozgenel, Firat and Caggioni, Francesco and Kanayet, Frank and Seide, Frank and Florez, Gabriela Medina and Schwarz, Gabriella and Badeer, Gada and Swee, Georgia and Halpern, Gil and Herman, Grant and Sizov, Grigory and Guangyi and Zhang and Lakshminarayanan, Guna and Inan, Hakan and Shojanazeri, Hamid and Zou, Han and Wang, Hannah and Zha, Hanwen and Habeeb, Haroun and Rudolph, Harrison and Suk, Helen and Aspegren, Henry and Goldman, Hunter and Zhan, Hongyuan and Damlaj, Ibrahim and Molybog, Igor and Tufanov, Igor and Leontiadis, Ilias and Veliche, Irina-Elena and Gat, Itai and Weissman, Jake and Geboski, James and Kohli, James and Lam, Janice and Asher, Japhet and Gaya, Jean-Baptiste and Marcus, Jeff and Tang, Jeff and Chan, Jennifer and Zhen, Jenny and Reizenstein, Jeremy and Teboul, Jeremy and Zhong, Jessica and Jin, Jian and Yang, Jingyi and Cummings, Joe and Carvill, Jon and Shepard, Jon and McPhie, Jonathan and Torres, Jonathan and Ginsburg, Josh and Wang, Junjie and Wu, Kai and U, Kam Hou and Saxena, Karan and Khandelwal, Kartikay and Zand, Katayoun and Matosich, Kathy and Veeraraghavan, Kaushik and Michelena, Kelly and Li, Keqian and Jagadeesh, Kiran and Huang, Kun and Chawla, Kunal and Huang, Kyle and Chen, Lailin and Garg, Lakshya and A, Lavender and Silva, Leandro and Bell, Lee and Zhang, Lei and Guo, Liangpeng and Yu, Licheng and Moshkovich, Liron and Wehrstedt, Luca and Khabsa, Madian and Avalani, Manav and Bhatt, Manish and Mankus, Martynas and Hasson, Matan and Lennie, Matthew and Reso, Matthias and Groshev, Maxim and Naumov, Maxim and Lathi, Maya and Keneally, Meghan and Liu, Miao and Seltzer, Michael L. and Valko, Michal and Restrepo, Michelle and Patel, Mihir and Vyatskov, Mik and Samvelyan, Mikayel and Clark, Mike and Macey, Mike and Wang, Mike and Hermoso, Miquel Jubert and Metanat, Mo and Rastegari, Mohammad and Bansal, Munish and Santhanam, Nandhini and Parks, Natascha and White, Natasha and Bawa, Navyata and Singhal, Nayan and Egebo, Nick and Usunier, Nicolas and Mehta, Nikhil and Laptev, Nikolay Pavlovich and Dong, Ning and Cheng, Norman and Chernoguz, Oleg and Hart, Olivia and Salpekar, Omkar and Kalinli, Ozlem and Kent, Parkin and Parekh, Parth and Saab, Paul and Balaji, Pavan and Rittner, Pedro and Bontrager, Philip and Roux, Pierre and Dollar, Piotr and Zvyagina, Polina and Ratanchandani, Prashant and Yuvraj, Pritish and Liang, Qian and Alao, Rachad and Rodriguez, Rachel and Ayub, Rafi and Murthy, Raghotham and Nayani, Raghu and Mitra, Rahul and Parthasarathy, Rangaprabhu and Li, Raymond and Hogan, Rebekkah and Battey, Robin and Wang, Rocky and Howes, Russ and Rinott, Ruty and Mehta, Sachin and Siby, Sachin and Bondu, Sai Jayesh and Datta, Samyak and Chugh, Sara and Hunt, Sara and Dhillon, Sargun and Sidorov, Sasha and Pan, Satadru and Mahajan, Saurabh and Verma, Saurabh and Yamamoto, Seiji and Ramaswamy, Sharadh and Lindsay, Shaun and Lindsay, Shaun and Feng, Sheng and Lin, Shenghao and Zha, Shengxin Cindy and Patil, Shishir and Shankar, Shiva and Zhang, Shuqiang and Zhang, Shuqiang and Wang, Sinong and Agarwal, Sneha and Sajuyigbe, Soji and Chintala, Soumith and Max, Stephanie and Chen, Stephen and Kehoe, Steve and Satterfield, Steve and Govindaprasad, Sudarshan and Gupta, Sumit and Deng, Summer and Cho, Sungmin and Virk, Sunny and Subramanian, Suraj and Choudhury, Sy and Goldman, Sydney and Remez, Tal and Glaser, Tamar and Best, Tamara and Koehler, Thilo and Robinson, Thomas and Li, Tianhe and Zhang, Tianjun and Matthews, Tim and Chou, Timothy and Shaked, Tzook and Vontimitta, Varun and Ajayi, Victoria and Montanez, Victoria and Mohan, Vijai and Kumar, Vinay Satish and Mangla, Vishal and Ionescu, Vlad and Poenaru, Vlad and Mihailescu, Vlad Tiberiu and Ivanov, Vladimir and Li, Wei and Wang, Wenchen and Jiang, Wenwen and Bouaziz, Wes and Constable, Will and Tang, Xiaocheng and Wu, Xiaojian and Wang, Xiaolan and Wu, Xilun and Gao, Xinbo and Kleinman, Yaniv and Chen, Yanjun and Hu, Ye and Jia, Ye and Qi, Ye and Li, Yenda and Zhang, Yilin and Zhang, Ying and Adi, Yossi and Nam, Youngjin and Yu and Wang and Zhao, Yu and Hao, Yuchen and Qian, Yundi and Li, Yunlu and He, Yuzi and Rait, Zach and DeVito, Zachary and Rosnbrick, Zef and Wen, Zhaoduo and Yang, Zhenyu and Zhao, Zhiwei and Ma, Zhiyu},
	month = nov,
	year = {2024},
	note = {arXiv:2407.21783 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition},
}

@misc{touvron_llama_2023,
	title = {Llama 2: {Open} {Foundation} and {Fine}-{Tuned} {Chat} {Models}},
	shorttitle = {Llama 2},
	url = {http://arxiv.org/abs/2307.09288},
	doi = {10.48550/arXiv.2307.09288},
	abstract = {In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closed-source models. We provide a detailed description of our approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work and contribute to the responsible development of LLMs.},
	urldate = {2025-10-19},
	publisher = {arXiv},
	author = {Touvron, Hugo and Martin, Louis and Stone, Kevin and Albert, Peter and Almahairi, Amjad and Babaei, Yasmine and Bashlykov, Nikolay and Batra, Soumya and Bhargava, Prajjwal and Bhosale, Shruti and Bikel, Dan and Blecher, Lukas and Ferrer, Cristian Canton and Chen, Moya and Cucurull, Guillem and Esiobu, David and Fernandes, Jude and Fu, Jeremy and Fu, Wenyin and Fuller, Brian and Gao, Cynthia and Goswami, Vedanuj and Goyal, Naman and Hartshorn, Anthony and Hosseini, Saghar and Hou, Rui and Inan, Hakan and Kardas, Marcin and Kerkez, Viktor and Khabsa, Madian and Kloumann, Isabel and Korenev, Artem and Koura, Punit Singh and Lachaux, Marie-Anne and Lavril, Thibaut and Lee, Jenya and Liskovich, Diana and Lu, Yinghai and Mao, Yuning and Martinet, Xavier and Mihaylov, Todor and Mishra, Pushkar and Molybog, Igor and Nie, Yixin and Poulton, Andrew and Reizenstein, Jeremy and Rungta, Rashi and Saladi, Kalyan and Schelten, Alan and Silva, Ruan and Smith, Eric Michael and Subramanian, Ranjan and Tan, Xiaoqing Ellen and Tang, Binh and Taylor, Ross and Williams, Adina and Kuan, Jian Xiang and Xu, Puxin and Yan, Zheng and Zarov, Iliyan and Zhang, Yuchen and Fan, Angela and Kambadur, Melanie and Narang, Sharan and Rodriguez, Aurelien and Stojnic, Robert and Edunov, Sergey and Scialom, Thomas},
	month = jul,
	year = {2023},
	note = {arXiv:2307.09288 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language},
}

@misc{brandom_scale_2024,
	title = {Scale {AI}’s {Remotasks} platform is dropping whole countries without explanation},
	url = {https://restofworld.org/2024/scale-ai-remotasks-banned-workers/},
	abstract = {Workers in Kenya, Nigeria, and Pakistan were booted off the AI-training service earlier this month.},
	language = {en-US},
	urldate = {2025-10-19},
	journal = {Rest of World},
	author = {Brandom, Russell},
	month = mar,
	year = {2024},
}

@misc{wangari_how_2025,
	title = {How {Big} {Tech} hides its outsourced {African} workforce},
	url = {https://restofworld.org/2025/big-tech-ai-labor-supply-chain-african-workers/},
	abstract = {New data reveals the hidden network of African workers powering AI, as they push for transparency from the global companies that employ them indirectly.},
	language = {en-US},
	urldate = {2025-10-19},
	journal = {Rest of World},
	author = {Wangari, Stephanie and Vaidyanathan, Gayathri},
	month = apr,
	year = {2025},
}

@misc{kousha_how_2025,
	title = {How much are {LLMs} changing the language of academic papers after {ChatGPT}? {A} multi-database and full text analysis},
	shorttitle = {How much are {LLMs} changing the language of academic papers after {ChatGPT}?},
	url = {http://arxiv.org/abs/2509.09596},
	doi = {10.48550/arXiv.2509.09596},
	abstract = {This study investigates how Large Language Models (LLMs) are influencing the language of academic papers by tracking 12 LLM-associated terms across six major scholarly databases (Scopus, Web of Science, PubMed, PubMed Central (PMC), Dimensions, and OpenAlex) from 2015 to 2024. Using over 2.4 million PMC open-access publications (2021-July 2025), we also analysed full texts to assess changes in the frequency and co-occurrence of these terms before and after ChatGPT's initial public release. Across databases, delve (+1,500\%), underscore (+1,000\%), and intricate (+700\%) had the largest increases between 2022 and 2024. Growth in LLM-term usage was much higher in STEM fields than in social sciences and arts and humanities. In PMC full texts, the proportion of papers using underscore six or more times increased by over 10,000\% from 2022 to 2025, followed by intricate (+5,400\%) and meticulous (+2,800\%). Nearly half of all 2024 PMC papers using any LLM term also included underscore, compared with only 3\%-14\% of papers before ChatGPT in 2022. Papers using one LLM term are now much more likely to include other terms. For example, in 2024, underscore strongly correlated with pivotal (0.449) and delve (0.311), compared with very weak associations in 2022 (0.032 and 0.018, respectively). These findings provide the first large-scale evidence based on full-text publications and multiple databases that some LLM-related terms are now being used much more frequently and together. The rapid uptake of LLMs to support scholarly publishing is a welcome development reducing the language barrier to academic publishing for non-English speakers.},
	urldate = {2025-10-19},
	publisher = {arXiv},
	author = {Kousha, Kayvan and Thelwall, Mike},
	month = sep,
	year = {2025},
	note = {arXiv:2509.09596 [cs]},
	keywords = {Computer Science - Digital Libraries},
}

@misc{noauthor_moving_nodate,
	title = {Moving toward truly responsible {AI} development in the global {AI} market},
	url = {https://www.brookings.edu/articles/moving-toward-truly-responsible-ai-development-in-the-global-ai-market/},
	abstract = {Chinasa T. Okolo and Marie Tano describe how corporations and governments can better respect the dignity of data workers in the Global South.},
	language = {en-US},
	urldate = {2025-10-19},
	journal = {Brookings},
}

@article{hern_techscape_2024,
	chapter = {Technology},
	title = {{TechScape}: {How} cheap, outsourced labour in {Africa} is shaping {AI} {English}},
	issn = {0261-3077},
	shorttitle = {{TechScape}},
	url = {https://www.theguardian.com/technology/2024/apr/16/techscape-ai-gadgest-humane-ai-pin-chatgpt},
	abstract = {Workers in Africa have been exploited first by being paid a pittance to help make chatbots, then by having their own words become AI-ese. Plus, new AI gadgets are coming for your smartphones},
	language = {en-GB},
	urldate = {2025-10-19},
	journal = {The Guardian},
	author = {Hern, Alex},
	month = apr,
	year = {2024},
	keywords = {Africa, Artificial intelligence (AI), ChatGPT, Computing, Gadgets, Kenya, Language, Nigeria, OpenAI, Technology},
}

@misc{juzek_word_2025,
	title = {Word {Overuse} and {Alignment} in {Large} {Language} {Models}: {The} {Influence} of {Learning} from {Human} {Feedback}},
	shorttitle = {Word {Overuse} and {Alignment} in {Large} {Language} {Models}},
	url = {http://arxiv.org/abs/2508.01930},
	doi = {10.17605/OSF.IO/JY48S},
	abstract = {Large Language Models (LLMs) are known to overuse certain terms like "delve" and "intricate." The exact reasons for these lexical choices, however, have been unclear. Using Meta's Llama model, this study investigates the contribution of Learning from Human Feedback (LHF), under which we subsume Reinforcement Learning from Human Feedback and Direct Preference Optimization. We present a straightforward procedure for detecting the lexical preferences of LLMs that are potentially LHF-induced. Next, we more conclusively link LHF to lexical overuse by experimentally emulating the LHF procedure and demonstrating that participants systematically prefer text variants that include certain words. This lexical overuse can be seen as a sort of misalignment, though our study highlights the potential divergence between the lexical expectations of different populations -- namely LHF workers versus LLM users. Our work contributes to the growing body of research on explainable artificial intelligence and emphasizes the importance of both data and procedural transparency in alignment research.},
	urldate = {2025-10-19},
	author = {Juzek, Tom S. and Ward, Zina B.},
	year = {2025},
	note = {arXiv:2508.01930 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language},
}

@misc{lu_ai_2025,
	title = {{AI} as {Humanity}'s {Salieri}: {Quantifying} {Linguistic} {Creativity} of {Language} {Models} via {Systematic} {Attribution} of {Machine} {Text} against {Web} {Text}},
	shorttitle = {{AI} as {Humanity}'s {Salieri}},
	url = {http://arxiv.org/abs/2410.04265},
	doi = {10.48550/arXiv.2410.04265},
	abstract = {Creativity has long been considered one of the most difficult aspect of human intelligence for AI to mimic. However, the rise of Large Language Models (LLMs), like ChatGPT, has raised questions about whether AI can match or even surpass human creativity. We present CREATIVITY INDEX as the first step to quantify the linguistic creativity of a text by reconstructing it from existing text snippets on the web. CREATIVITY INDEX is motivated by the hypothesis that the seemingly remarkable creativity of LLMs may be attributable in large part to the creativity of human-written texts on the web. To compute CREATIVITY INDEX efficiently, we introduce DJ SEARCH, a novel dynamic programming algorithm that can search verbatim and near-verbatim matches of text snippets from a given document against the web. Experiments reveal that the CREATIVITY INDEX of professional human authors is on average 66.2\% higher than that of LLMs, and that alignment reduces the CREATIVITY INDEX of LLMs by an average of 30.1\%. In addition, we find that distinguished authors like Hemingway exhibit measurably higher CREATIVITY INDEX compared to other human writers. Finally, we demonstrate that CREATIVITY INDEX can be used as a surprisingly effective criterion for zero-shot machine text detection, surpassing the strongest existing zero-shot system, DetectGPT, by a significant margin of 30.2\%, and even outperforming the strongest supervised system, GhostBuster, in five out of six domains.},
	urldate = {2025-10-19},
	publisher = {arXiv},
	author = {Lu, Ximing and Sclar, Melanie and Hallinan, Skyler and Mireshghallah, Niloofar and Liu, Jiacheng and Han, Seungju and Ettinger, Allyson and Jiang, Liwei and Chandu, Khyathi and Dziri, Nouha and Choi, Yejin},
	month = jan,
	year = {2025},
	note = {arXiv:2410.04265 [cs]},
	keywords = {Computer Science - Computation and Language},
}
