% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").

@book{Aho:72,
    author  = {Alfred V. Aho and Jeffrey D. Ullman},
    title   = {The Theory of Parsing, Translation and Compiling},
    year    = "1972",
    volume  = "1",
    publisher = {Prentice-Hall},
    address = {Englewood Cliffs, NJ}
}

@book{APA:83,
    author  = {{American Psychological Association}},
    title   = {Publications Manual},
    year    = "1983",
    publisher = {American Psychological Association},
    address = {Washington, DC}
}

@article{Chandra:81,
	author = {Ashok K. Chandra and Dexter C. Kozen and Larry J. Stockmeyer},
	year = "1981",
	title = {Alternation},
	journal = {Journal of the Association for Computing Machinery},
	volume = "28",
	number = "1",
	pages = "114--133",
	doi = "10.1145/322234.322243",
}

@inproceedings{andrew2007scalable,
  title={Scalable training of {L1}-regularized log-linear models},
  author={Andrew, Galen and Gao, Jianfeng},
  booktitle={Proceedings of the 24th International Conference on Machine Learning},
  pages={33--40},
  year={2007},
}

@book{Gusfield:97,
    author  = {Dan Gusfield},
    title   = {Algorithms on Strings, Trees and Sequences},
    year    = "1997",
    publisher = {Cambridge University Press},
    address = {Cambridge, UK}
}

@article{rasooli-tetrault-2015,
    author    = {Mohammad Sadegh Rasooli and Joel R. Tetreault},
    title     = {Yara Parser: {A} Fast and Accurate Dependency Parser},
    journal   = {Computing Research Repository},
    volume    = {arXiv:1503.06733},
    year      = {2015},
    url       = {http://arxiv.org/abs/1503.06733},
    note    = {version 2}
}

@article{Ando2005,
	Acmid = {1194905},
	Author = {Ando, Rie Kubota and Zhang, Tong},
	Issn = {1532-4435},
	Issue_Date = {12/1/2005},
	Journal = {Journal of Machine Learning Research},
	Month = dec,
	Numpages = {37},
	Pages = {1817--1853},
	Publisher = {JMLR.org},
	Title = {A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data},
	Volume = {6},
	Year = {2005}
}


@misc{naseem2026polarbenchmarkmultilingualmulticultural,
      title={POLAR: A Benchmark for Multilingual, Multicultural, and Multi-Event Online Polarization},
      author={Usman Naseem and Robert Geislinger and Juan Ren and Sarah Kohail and Rudy Garrido Veliz and P Sam Sahil and Yiran Zhang and Marco Antonio Stranisci and Idris Abdulmumin and Özge Alacam and Cengiz Acartürk and Aisha Jabr and Saba Anwar and Abinew Ali Ayele and Simona Frenda and Alessandra Teresa Cignarella and Elena Tutubalina and Oleg Rogov and Aung Kyaw Htet and Xintong Wang and Surendrabikram Thapa and Kritesh Rauniyar and Tanmoy Chakraborty and Arfeen Zeeshan and Dheeraj Kodati and Satya Keerthi and Sahar Moradizeyveh and Firoj Alam and Arid Hasan and Syed Ishtiaque Ahmed and Ye Kyaw Thu and Shantipriya Parida and Ihsan Ayyub Qazi and Lilian Wanzare and Nelson Odhiambo Onyango and Clemencia Siro and Jane Wanjiru Kimani and Ibrahim Said Ahmad and Adem Chanie Ali and Martin Semmann and Chris Biemann and Shamsuddeen Hassan Muhammad and Seid Muhie Yimam},
      year={2026},
      eprint={2505.20624},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.20624},
}

@phdthesis{garimella2018polarization,
    title = {{Polarization on Social Media}},
    author = "Kiran Garimella",
    year = "2018",
    isbn = "978-952-60-7832-8",
    series = "Aalto University publication series DOCTORAL DISSERTATIONS",
    publisher = "Aalto University",
    address = "Finland",
    school = "Aalto University",
    url = "http://urn.fi/URN:ISBN:978-952-60-7833-5"
}

@article{martinez2024methodology,
    title={{Methodology for Measuring Individual Affective Polarization Using Sentiment Analysis in Social Networks}},
    author={Mart{\'\i}nez-Espa{\~n}a, Raquel and Fern{\'a}ndez-Pedauye, Julio and de Luc{\'\i}a, Jos{\'e} Giner-P{\'e}rez and Rojo-Mart{\'\i}nez, Jose Miguel and Bakdid-Albane, Kaoutar and Garc{\'\i}a-Escribano, Juan Jos{\'e}},
    journal={IEEE Access},
    year={2024},
    publisher={IEEE},
    doi={10.1109/ACCESS.2024.3431999}
}



@article{Zia_Castro_Zubiaga_Tyson_2022,
	abstractnote = {Hate speech has proliferated on social media platforms in recent years. While this has been the focus of many studies, most works have exclusively focused on a single language, generally English. Low-resourced languages have been neglected due to the dearth of labeled resources. These languages, however, represent an important portion of the data due to the multilingual nature of social media. This work presents a novel zero-shot, cross-lingual transfer learning pipeline based on pseudo-label fine-tuning of Transformer Language Models for automatic hate speech detection. We employ our pipeline on benchmark datasets covering English (source) and 6 different non-English (target) languages written in 3 different scripts. Our pipeline achieves an average improvement of 7.6% (in terms of macro-F1) over previous zero-shot, cross-lingual models. This demonstrates the feasibility of high accuracy automatic hate speech detection for low-resource languages. We release our code and models at https://github.com/harisbinzia/ZeroshotCrosslingualHateSpeech.},
	author = {Zia, Haris Bin and Castro, Ignacio and Zubiaga, Arkaitz and Tyson, Gareth},
	doi = {10.1609/icwsm.v16i1.19402},
	journal = {Proceedings of the International AAAI Conference on Web and Social Media},
	month = {May},
	number = {1},
	pages = {1435-1439},
	title = {Improving Zero-Shot Cross-Lingual Hate Speech Detection with Pseudo-Label Fine-Tuning of Transformer Language Models},
	url = {https://ojs.aaai.org/index.php/ICWSM/article/view/19402},
	volume = {16},
	year = {2022},
	bdsk-url-1 = {https://ojs.aaai.org/index.php/ICWSM/article/view/19402},
	bdsk-url-2 = {https://doi.org/10.1609/icwsm.v16i1.19402}
}

@inproceedings{bruno-etal-2024-towards,
    title = "Towards a Hate Speech Index with Attention-based {LSTM}s and {XLM}-{R}o{BERT}a",
    author = "Bruno, Mauro  and
      Catanese, Elena  and
      Ortame, Francesco",
    editor = "Dell'Orletta, Felice  and
      Lenci, Alessandro  and
      Montemagni, Simonetta  and
      Sprugnoli, Rachele",
    booktitle = "Proceedings of the Tenth Italian Conference on Computational Linguistics (CLiC-it 2024)",
    month = dec,
    year = "2024",
    address = "Pisa, Italy",
    publisher = "CEUR Workshop Proceedings",
    url = "https://aclanthology.org/2024.clicit-1.14/",
    pages = "106--113",
    ISBN = "979-12-210-7060-6",
    abstract = "The uncontrolled diffusion of hate speech on social media requires robust detection mechanisms to measure its harmful impact. Analyzing texts from X (formerly Twitter) is challenging due to slang, neologisms, and sarcasm, which require advanced and intelligent detection approaches. While sophisticated models like large language models (LLMs) demonstrate impressive accuracy, their prohibitive inference times make it impractical to process millions of tweets. Therefore, we propose a mixed approach using a bidirectional long short-term memory model with an added attention mechanism (AT-BiLSTM) for improved natural language understanding. We benchmark this model against a standard BiLSTM model and a fine-tuned multilingual robustly optimized BERT (RoBERTa).The task of hate speech detection has been extensively explored in the EVALITA campaigns, which have achieved impressive results. Building on this foundation, we aim to develop a robust classifier to predict the content of approximately 20 million tweets related to immigration. The performance of our models is comparable to the top entries from the EVALITA campaigns, and we show the effects of training different networks on the dynamics of the Hate Speech Index (HSI). We also utilize a custom labeled dataset for benchmarking and training."
}

@inproceedings{antypas-camacho-collados-2023-robust,
    title = "Robust Hate Speech Detection in Social Media: A Cross-Dataset Empirical Evaluation",
    author = "Antypas, Dimosthenis  and
      Camacho-Collados, Jose",
    editor = "Chung, Yi-ling  and
      R{\{}{\textbackslash}{''}ottger{\}}, Paul  and
      Nozza, Debora  and
      Talat, Zeerak  and
      Mostafazadeh Davani, Aida",
    booktitle = "The 7th Workshop on Online Abuse and Harms (WOAH)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.woah-1.25/",
    doi = "10.18653/v1/2023.woah-1.25",
    pages = "231--242",
    abstract = "The automatic detection of hate speech online is an active research area in NLP. Most of the studies to date are based on social media datasets that contribute to the creation of hate speech detection models trained on them. However, data creation processes contain their own biases, and models inherently learn from these dataset-specific biases. In this paper, we perform a large-scale cross-dataset comparison where we fine-tune language models on different hate speech detection datasets. This analysis shows how some datasets are more generalizable than others when used as training data. Crucially, our experiments show how combining hate speech detection datasets can contribute to the development of robust hate speech detection models. This robustness holds even when controlling by data size and compared with the best individual datasets."
}


@inproceedings{conneau-etal-2020-unsupervised,
    title = "Unsupervised Cross-lingual Representation Learning at Scale",
    author = "Conneau, Alexis  and
      Khandelwal, Kartikay  and
      Goyal, Naman  and
      Chaudhary, Vishrav  and
      Wenzek, Guillaume  and
      Guzm{\'a}n, Francisco  and
      Grave, Edouard  and
      Ott, Myle  and
      Zettlemoyer, Luke  and
      Stoyanov, Veselin",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.acl-main.747/",
    doi = "10.18653/v1/2020.acl-main.747",
    pages = "8440--8451",
    abstract = "This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +14.6{\%} average accuracy on XNLI, +13{\%} average F1 score on MLQA, and +2.4{\%} F1 score on NER. XLM-R performs particularly well on low-resource languages, improving 15.7{\%} in XNLI accuracy for Swahili and 11.4{\%} for Urdu over previous XLM models. We also present a detailed empirical analysis of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing per-language performance; XLM-R is very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We will make our code and models publicly available."
}


@inproceedings{jaremko-etal-2025-revisiting,
    title = "Revisiting Implicitly Abusive Language Detection: Evaluating {LLM}s in Zero-Shot and Few-Shot Settings",
    author = "Jaremko, Julia  and
      Gromann, Dagmar  and
      Wiegand, Michael",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.coling-main.262/",
    pages = "3879--3898",
    abstract = "Implicitly abusive language (IAL), unlike its explicit counterpart, lacks overt slurs or unambiguously offensive keywords, such as ``bimbo'' or ``scum'', making it challenging to detect and mitigate. While current research predominantly focuses on explicitly abusive language, the subtler and more covert forms of IAL remain insufficiently studied. The rapid advancement and widespread adoption of large language models (LLMs) have opened new possibilities for various NLP tasks, but their application to IAL detection has been limited. We revisit three very recent challenging datasets of IAL and investigate the potential of LLMs to enhance the detection of IAL in English through zero-shot and few-shot prompting approaches. We evaluate the models' capabilities in classifying sentences directly as either IAL or benign, and in extracting linguistic features associated with IAL. Our results indicate that classifiers trained on features extracted by advanced LLMs outperform the best previously reported results, achieving near-human performance."
}

@inproceedings{tanwar-etal-2023-multilingual,
    title = "Multilingual {LLM}s are Better Cross-lingual In-context Learners with Alignment",
    author = "Tanwar, Eshaan  and
      Dutta, Subhabrata  and
      Borthakur, Manish  and
      Chakraborty, Tanmoy",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.346/",
    doi = "10.18653/v1/2023.acl-long.346",
    pages = "6292--6307",
    abstract = "In-context learning (ICL) unfolds as large language models become capable of inferring test labels conditioned on a few labeled samples without any gradient update. ICL-enabled large language models provide a promising step forward toward bypassing recurrent annotation costs in a low-resource setting. Yet, only a handful of past studies have explored ICL in a cross-lingual setting, in which the need for transferring label-knowledge from a high-resource language to a low-resource one is immensely crucial. To bridge the gap, we provide the first in-depth analysis of ICL for cross-lingual text classification. We find that the prevalent mode of selecting random input-label pairs to construct the prompt-context is severely limited in the case of cross-lingual ICL, primarily due to the lack of alignment in the input as well as the output spaces. To mitigate this, we propose a novel prompt construction strategy {---} Cross-lingual In-context Source Target Alignment (X-InSTA). With an injected coherence in the semantics of the input examples and a task-based alignment across the source and target languages, X-InSTA is able to outperform random prompt selection by a large margin across three different tasks using 44 different cross-lingual pairs."
}

@misc{berdichevsky2025salsasinglepassautoregressivellm,
      title={SALSA: Single-pass Autoregressive LLM Structured Classification},
      author={Ruslan Berdichevsky and Shai Nahum-Gefen and Elad Ben Zaken},
      year={2025},
      eprint={2510.22691},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2510.22691},
}

@inproceedings{xue-etal-2025-jnlp,
    title = "{JNLP} at {S}em{E}val-2025 Task 11: Cross-Lingual Multi-Label Emotion Detection Using Generative Models",
    author = "Xue, Jieying  and
      Nguyen, Phuong  and
      Nguyen, Minh  and
      Liu, Xin",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.semeval-1.4/",
    pages = "20--27",
    ISBN = "979-8-89176-273-2",
    abstract = "With the rapid advancement of global digitalization, users from different countries increasingly rely on social media for information exchange. In this context, multilingual multi-label emotion detection has emerged as a critical research area.This study addresses SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection. Our paper focuses on two sub-tracks of this task: (1) Track A: Multi-label emotion detection, and (2) Track B: Emotion intensity.To tackle multilingual challenges, we leverage pre-trained multilingual models and focus on two architectures: (1) a fine-tuned BERT-based classification model and (2) an instruction-tuned generative LLM. Additionally, we propose two methods for handling multi-label classification: the Base method, which maps an input directly to all its corresponding emotion labels, and the Pairwise method, which models the relationship between the input text and each emotion category individually.Experimental results demonstrate the strong generalization ability of our approach in multilingual emotion recognition. In Track A, our method achieved Top 4 performance across 10 languages, ranking 1st in Hindi language. In Track B, our approach also secured Top 5 performance in 7 languages, highlighting its simplicity and effectiveness."
}

@inproceedings{muhammad-etal-2025-semeval,
    title = "{S}em{E}val-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection",
    author = "Muhammad, Shamsuddeen Hassan  and
      Ousidhoum, Nedjma  and
      Abdulmumin, Idris  and
      Yimam, Seid Muhie  and
      Wahle, Jan Philip  and
      Lima Ruas, Terry  and
      Beloucif, Meriem  and
      De Kock, Christine  and
      Belay, Tadesse Destaw  and
      Ahmad, Ibrahim Said  and
      Surange, Nirmal  and
      Teodorescu, Daniela  and
      Adelani, David Ifeoluwa  and
      Aji, Alham Fikri  and
      Ali, Felermino Dario Mario  and
      Araujo, Vladimir  and
      Ayele, Abinew Ali  and
      Ignat, Oana  and
      Panchenko, Alexander  and
      Zhou, Yi  and
      Mohammad, Saif",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.semeval-1.327/",
    pages = "2558--2569",
    ISBN = "979-8-89176-273-2",
    abstract = "We present our shared task on text-based emotion detection, covering more than 30 languages from seven distinct language families. These languages are predominantly low-resource and spoken across various continents. The data instances are multi-labeled into six emotional classes, with additional datasets in 11 languages annotated for emotion intensity. Participants were asked to predict labels in three tracks: (a) emotion labels in monolingual settings, (b) emotion intensity scores, and (c) emotion labels in cross-lingual settings."
}


@misc{marone2025mmbertmodernmultilingualencoder,
      title={mmBERT: A Modern Multilingual Encoder with Annealed Language Learning},
      author={Marc Marone and Orion Weller and William Fleshman and Eugene Yang and Dawn Lawrie and Benjamin Van Durme},
      year={2025},
      eprint={2509.06888},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2509.06888},
}


@misc{gemmateam2025gemma3technicalreport,
      title={Gemma 3 Technical Report},
      author={Gemma Team and Aishwarya Kamath and Johan Ferret and Shreya Pathak and Nino Vieillard and Ramona Merhej and Sarah Perrin and Tatiana Matejovicova and Alexandre Ramé and Morgane Rivière and Louis Rouillard and Thomas Mesnard and Geoffrey Cideron and Jean-bastien Grill and Sabela Ramos and Edouard Yvinec and Michelle Casbon and Etienne Pot and Ivo Penchev and Gaël Liu and Francesco Visin and Kathleen Kenealy and Lucas Beyer and Xiaohai Zhai and Anton Tsitsulin and Robert Busa-Fekete and Alex Feng and Noveen Sachdeva and Benjamin Coleman and Yi Gao and Basil Mustafa and Iain Barr and Emilio Parisotto and David Tian and Matan Eyal and Colin Cherry and Jan-Thorsten Peter and Danila Sinopalnikov and Surya Bhupatiraju and Rishabh Agarwal and Mehran Kazemi and Dan Malkin and Ravin Kumar and David Vilar and Idan Brusilovsky and Jiaming Luo and Andreas Steiner and Abe Friesen and Abhanshu Sharma and Abheesht Sharma and Adi Mayrav Gilady and Adrian Goedeckemeyer and Alaa Saade and Alex Feng and Alexander Kolesnikov and Alexei Bendebury and Alvin Abdagic and Amit Vadi and András György and André Susano Pinto and Anil Das and Ankur Bapna and Antoine Miech and Antoine Yang and Antonia Paterson and Ashish Shenoy and Ayan Chakrabarti and Bilal Piot and Bo Wu and Bobak Shahriari and Bryce Petrini and Charlie Chen and Charline Le Lan and Christopher A. Choquette-Choo and CJ Carey and Cormac Brick and Daniel Deutsch and Danielle Eisenbud and Dee Cattle and Derek Cheng and Dimitris Paparas and Divyashree Shivakumar Sreepathihalli and Doug Reid and Dustin Tran and Dustin Zelle and Eric Noland and Erwin Huizenga and Eugene Kharitonov and Frederick Liu and Gagik Amirkhanyan and Glenn Cameron and Hadi Hashemi and Hanna Klimczak-Plucińska and Harman Singh and Harsh Mehta and Harshal Tushar Lehri and Hussein Hazimeh and Ian Ballantyne and Idan Szpektor and Ivan Nardini and Jean Pouget-Abadie and Jetha Chan and Joe Stanton and John Wieting and Jonathan Lai and Jordi Orbay and Joseph Fernandez and Josh Newlan and Ju-yeong Ji and Jyotinder Singh and Kat Black and Kathy Yu and Kevin Hui and Kiran Vodrahalli and Klaus Greff and Linhai Qiu and Marcella Valentine and Marina Coelho and Marvin Ritter and Matt Hoffman and Matthew Watson and Mayank Chaturvedi and Michael Moynihan and Min Ma and Nabila Babar and Natasha Noy and Nathan Byrd and Nick Roy and Nikola Momchev and Nilay Chauhan and Noveen Sachdeva and Oskar Bunyan and Pankil Botarda and Paul Caron and Paul Kishan Rubenstein and Phil Culliton and Philipp Schmid and Pier Giuseppe Sessa and Pingmei Xu and Piotr Stanczyk and Pouya Tafti and Rakesh Shivanna and Renjie Wu and Renke Pan and Reza Rokni and Rob Willoughby and Rohith Vallu and Ryan Mullins and Sammy Jerome and Sara Smoot and Sertan Girgin and Shariq Iqbal and Shashir Reddy and Shruti Sheth and Siim Põder and Sijal Bhatnagar and Sindhu Raghuram Panyam and Sivan Eiger and Susan Zhang and Tianqi Liu and Trevor Yacovone and Tyler Liechty and Uday Kalra and Utku Evci and Vedant Misra and Vincent Roseberry and Vlad Feinberg and Vlad Kolesnikov and Woohyun Han and Woosuk Kwon and Xi Chen and Yinlam Chow and Yuvein Zhu and Zichuan Wei and Zoltan Egyed and Victor Cotruta and Minh Giang and Phoebe Kirk and Anand Rao and Kat Black and Nabila Babar and Jessica Lo and Erica Moreira and Luiz Gustavo Martins and Omar Sanseviero and Lucas Gonzalez and Zach Gleicher and Tris Warkentin and Vahab Mirrokni and Evan Senter and Eli Collins and Joelle Barral and Zoubin Ghahramani and Raia Hadsell and Yossi Matias and D. Sculley and Slav Petrov and Noah Fiedel and Noam Shazeer and Oriol Vinyals and Jeff Dean and Demis Hassabis and Koray Kavukcuoglu and Clement Farabet and Elena Buchatskaya and Jean-Baptiste Alayrac and Rohan Anil and Dmitry and Lepikhin and Sebastian Borgeaud and Olivier Bachem and Armand Joulin and Alek Andreev and Cassidy Hardin and Robert Dadashi and Léonard Hussenot},
      year={2025},
      eprint={2503.19786},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2503.19786},
}


@inproceedings{hu2022lora,
title={Lo{RA}: Low-Rank Adaptation of Large Language Models},
author={Edward J Hu and yelong shen and Phillip Wallis and Zeyuan Allen-Zhu and Yuanzhi Li and Shean Wang and Lu Wang and Weizhu Chen},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=nZeVKeeFYf9}
}


@inproceedings{naseem-etal-2026-semeval,
    title     = {{SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization}},
    author    = {Naseem, Usman and Geislinger, Robert and Ren, Juan and Kohail, Sarah and Garrido Veliz, Rudy and Sam Sahil, P and Zhang, Yiran and Stranisci, Marco Antonio and Abdulmumin, Idris and Alacam, Özge and Acarürk, Cengiz and Jabr, Aisha and Anwar, Saba and Ayele, Abinew Ali and Tutubalina, Elena and Htet, Aung Kyaw and Wang, Xintong and Thapa, Surendrabikram and Chakraborty, Tanmoy and Kodati, Dheeraj and Moradizeyveh, Sahar and Alam, Firoj and Thu, Ye Kyaw and Parida, Shantipriya and Qazi, Ihsan Ayyub and Onyango, Nelson Odhiambo and Siro, Clemencia and Ahmad, Ibrahim Said and Wanzare, Lilian and Ali, Adem Chanie and Semmann, Martin and Biemann, Chris and Muhammad, Shamsuddeen Hassan and Yimam, Seid Muhie},
    booktitle = {{Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)}},
    year      = {2026},
    publisher = {{Association for Computational Linguistics}},
    address = {{San Diego, CA, USA}}
}

@inproceedings{wongso-etal-2025-lazarus,
    title = "Lazarus {NLP} at {S}em{E}val-2025 Task 11: Fine-Tuning Large Language Models for Multi-Label Emotion Classification via Sentence-Label Pairing",
    author = "Wongso, Wilson  and
      Setiawan, David  and
      Joyoadikusumo, Ananto  and
      Limcorn, Steven",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.semeval-1.104/",
    pages = "763--772",
    ISBN = "979-8-89176-273-2",
    abstract = "Multi-label emotion classification in low-resource languages remains challenging due to limited annotated data and model adaptability. To address this, we fine-tune large language models (LLMs) using a sentence-label pairing approach, optimizing efficiency while improving classification performance. Evaluating on Sundanese, Indonesian, and Javanese, our method outperforms conventional classifier-based fine-tuning and achieves strong zero-shot cross-lingual transfer. Notably, our approach ranks first in the Sundanese subset of SemEval-2025 Task 11 Track A. Our findings demonstrate the effectiveness of LLM fine-tuning for low-resource emotion classification, underscoring the importance of tailoring adaptation strategies to specific language families in multilingual contexts."
}
