@article{alexanderOverviewInpatientCoding2003,
  title = {Overview of Inpatient Coding},
  author = {Alexander, Sherri and Conner, Therese and Slaughter, Teresa},
  year = {2003},
  month = nov,
  journal = {American journal of health-system pharmacy: AJHP: official journal of the American Society of Health-System Pharmacists},
  volume = {60},
  number = {21 Suppl 6},
  pages = {S11-14},
  issn = {1079-2082},
  doi = {10.1093/ajhp/60.suppl_6.S11},
  langid = {english},
  pmid = {14619128}
}

@inproceedings{ramesh-kashyap-etal-2024-comprehensive,
    title = "A Comprehensive Survey of Sentence Representations: From the {BERT} Epoch to the {CHATGPT} Era and Beyond",
    author = "Ramesh Kashyap, Abhinav  and
      Nguyen, Thanh-Tung  and
      Schlegel, Viktor  and
      Winkler, Stefan  and
      Ng, See-Kiong  and
      Poria, Soujanya",
    editor = "Graham, Yvette  and
      Purver, Matthew",
    booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2024",
    address = "St. Julian{'}s, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.eacl-long.104/",
    pages = "1738--1751",
    abstract = "Sentence representations are a critical component in NLP applications such as retrieval, question answering, and text classification. They capture the meaning of a sentence, enabling machines to understand and reason over human language. In recent years, significant progress has been made in developing methods for learning sentence representations, including unsupervised, supervised, and transfer learning approaches. However there is no literature review on sentence representations till now. In this paper, we provide an overview of the different methods for sentence representation learning, focusing mostly on deep learning models. We provide a systematic organization of the literature, highlighting the key contributions and challenges in this area. Overall, our review highlights the importance of this area in natural language processing, the progress made in sentence representation learning, and the challenges that remain. We conclude with directions for future research, suggesting potential avenues for improving the quality and efficiency of sentence representations."
}

@article{yang2019xlnet,
  title={Xlnet: Generalized autoregressive pretraining for language understanding},
  author={Yang, Zhilin and Dai, Zihang and Yang, Yiming and Carbonell, Jaime and Salakhutdinov, Russ R and Le, Quoc V},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}

@inproceedings{beltagy-etal-2019-scibert,
    title = "{S}ci{BERT}: A Pretrained Language Model for Scientific Text",
    author = "Beltagy, Iz  and
      Lo, Kyle  and
      Cohan, Arman",
    editor = "Inui, Kentaro  and
      Jiang, Jing  and
      Ng, Vincent  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D19-1371/",
    doi = "10.18653/v1/D19-1371",
    pages = "3615--3620",
    abstract = "Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive. We release SciBERT, a pretrained language model based on BERT (Devlin et. al., 2018) to address the lack of high-quality, large-scale labeled scientific data. SciBERT leverages unsupervised pretraining on a large multi-domain corpus of scientific publications to improve performance on downstream scientific NLP tasks. We evaluate on a suite of tasks including sequence tagging, sentence classification and dependency parsing, with datasets from a variety of scientific domains. We demonstrate statistically significant improvements over BERT and achieve new state-of-the-art results on several of these tasks. The code and pretrained models are available at \url{https://github.com/allenai/scibert/}."
}

@article{bm25,
author = {Robertson, Stephen and Zaragoza, Hugo},
title = {The Probabilistic Relevance Framework: BM25 and Beyond},
year = {2009},
issue_date = {April 2009},
publisher = {Now Publishers Inc.},
address = {Hanover, MA, USA},
volume = {3},
number = {4},
issn = {1554-0669},
url = {https://doi.org/10.1561/1500000019},
doi = {10.1561/1500000019},
abstract = {The Probabilistic Relevance Framework (PRF) is a formal framework for document retrieval, grounded in work done in the 1970—1980s, which led to the development of one of the most successful text-retrieval algorithms, BM25. In recent years, research in the PRF has yielded new retrieval models capable of taking into account document meta-data (especially structure and link-graph information). Again, this has led to one of the most successful Web-search and corporate-search algorithms, BM25F. This work presents the PRF from a conceptual point of view, describing the probabilistic modelling assumptions behind the framework and the different ranking algorithms that result from its application: the binary independence model, relevance feedback models, BM25 and BM25F. It also discusses the relation between the PRF and other statistical models for IR, and covers some related topics, such as the use of non-textual features, and parameter optimisation for models with free parameters.},
journal = {Found. Trends Inf. Retr.},
month = apr,
pages = {333–389},
numpages = {57}
}

@article{alsentzer2019clincalbert,
  author       = {Emily Alsentzer and
                  John R. Murphy and
                  Willie Boag and
                  Wei{-}Hung Weng and
                  Di Jin and
                  Tristan Naumann and
                  Matthew B. A. McDermott},
  title        = {Publicly Available Clinical {BERT} Embeddings},
  journal      = {CoRR},
  volume       = {abs/1904.03323},
  year         = {2019},
  url          = {http://arxiv.org/abs/1904.03323},
  eprinttype    = {arXiv},
  eprint       = {1904.03323},
  timestamp    = {Wed, 24 Apr 2019 12:21:25 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1904-03323.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{wang2020minilm,
  title={Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers},
  author={Wang, Wenhui and Wei, Furu and Dong, Li and Bao, Hangbo and Yang, Nan and Zhou, Ming},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={5776--5788},
  year={2020}
}

@article{wornow2023shaky,
  title={The shaky foundations of large language models and foundation models for electronic health records},
  author={Wornow, Michael and Xu, Yizhe and Thapa, Rahul and Patel, Birju and Steinberg, Ethan and Fleming, Scott and Pfeffer, Michael A and Fries, Jason and Shah, Nigam H},
  journal={npj digital medicine},
  volume={6},
  number={1},
  pages={135},
  year={2023},
  publisher={Nature Publishing Group UK London}
}

@inproceedings{nigatu-etal-2024-zenos,
    title = "The Zeno`s Paradox of {\textquoteleft}Low-Resource' Languages",
    author = "Nigatu, Hellina Hailu  and
      Tonja, Atnafu Lambebo  and
      Rosman, Benjamin  and
      Solorio, Thamar  and
      Choudhury, Monojit",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.emnlp-main.983/",
    doi = "10.18653/v1/2024.emnlp-main.983",
    pages = "17753--17774",
    abstract = "The disparity in the languages commonly studied in Natural Language Processing (NLP) is typically reflected by referring to languages as low vs high-resourced. However, there is limited consensus on what exactly qualifies as a {\textquoteleft}low-resource language.' To understand how NLP papers define and study {\textquoteleft}low resource' languages, we qualitatively analyzed 150 papers from the ACL Anthology and popular speech-processing conferences that mention the keyword {\textquoteleft}low-resource.' Based on our analysis, we show how several interacting axes contribute to {\textquoteleft}low-resourcedness' of a language and why that makes it difficult to track progress for each individual language. We hope our work (1) elicits explicit definitions of the terminology when it is used in papers and (2) provides grounding for the different axes to consider when connoting a language as low-resource."
}

@article{henderson2017efficient,
  title={Efficient natural language response suggestion for smart reply},
  author={Henderson, Matthew and Al-Rfou, Rami and Strope, Brian and Sung, Yun-Hsuan and Luk{\'a}cs, L{\'a}szl{\'o} and Guo, Ruiqi and Kumar, Sanjiv and Miklos, Balint and Kurzweil, Ray},
  journal={arXiv preprint arXiv:1705.00652},
  year={2017}
}

@inproceedings{muennighoff-etal-2023-mteb,
    title = "{MTEB}: Massive Text Embedding Benchmark",
    author = "Muennighoff, Niklas  and
      Tazi, Nouamane  and
      Magne, Loic  and
      Reimers, Nils",
    editor = "Vlachos, Andreas  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.eacl-main.148/",
    doi = "10.18653/v1/2023.eacl-main.148",
    pages = "2014--2037",
    abstract = "Text embeddings are commonly evaluated on a small set of datasets from a single task not covering their possible applications to other tasks. It is unclear whether state-of-the-art embeddings on semantic textual similarity (STS) can be equally well applied to other tasks like clustering or reranking. This makes progress in the field difficult to track, as various models are constantly being proposed without proper evaluation. To solve this problem, we introduce the Massive Text Embedding Benchmark (MTEB). MTEB spans 8 embedding tasks covering a total of 58 datasets and 112 languages. Through the benchmarking of 33 models on MTEB, we establish the most comprehensive benchmark of text embeddings todate. We find that no particular text embedding method dominates across all tasks. This suggests that the field has yet to converge on a universal text embedding method and scale it up sufficiently to provide state-of-theart results on all embedding tasks. MTEB comes with open-source code and a public leaderboard at \url{https://github.com/embeddings-benchmark/mteb}."
}

@inproceedings{lewis2020pretrained,
  title={Pretrained language models for biomedical and clinical tasks: understanding and extending the state-of-the-art},
  author={Lewis, Patrick and Ott, Myle and Du, Jingfei and Stoyanov, Veselin},
  booktitle={Proceedings of the 3rd clinical natural language processing workshop},
  pages={146--157},
  year={2020}
}

@article{lee2019biobert,
    author = {Lee, Jinhyuk and Yoon, Wonjin and Kim, Sungdong and Kim, Donghyeon and Kim, Sunkyu and So, Chan Ho and Kang, Jaewoo},
    title = {BioBERT: a pre-trained biomedical language representation model for biomedical text mining},
    journal = {Bioinformatics},
    volume = {36},
    number = {4},
    pages = {1234-1240},
    year = {2019},
    month = {09},
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btz682},
    url = {https://doi.org/10.1093/bioinformatics/btz682},
    eprint = {https://academic.oup.com/bioinformatics/article-pdf/36/4/1234/48983216/bioinformatics\_36\_4\_1234.pdf},
}




@article{rasmy2021med,
  title={Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction},
  author={Rasmy, Laila and Xiang, Yang and Xie, Ziqian and Tao, Cui and Zhi, Degui},
  journal={NPJ digital medicine},
  volume={4},
  number={1},
  pages={86},
  year={2021},
  publisher={Nature Publishing Group UK London}
}

@inproceedings{masala-etal-2020-robert,
    title = "{R}o{BERT} {--} A {R}omanian {BERT} Model",
    author = "Masala, Mihai  and
      Ruseti, Stefan  and
      Dascalu, Mihai",
    editor = "Scott, Donia  and
      Bel, Nuria  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2020.coling-main.581/",
    doi = "10.18653/v1/2020.coling-main.581",
    pages = "6626--6637",
    abstract = "Deep pre-trained language models tend to become ubiquitous in the field of Natural Language Processing (NLP). These models learn contextualized representations by using a huge amount of unlabeled text data and obtain state of the art results on a multitude of NLP tasks, by enabling efficient transfer learning. For other languages besides English, there are limited options of such models, most of which are trained only on multi-lingual corpora. In this paper we introduce a Romanian-only pre-trained BERT model {--} RoBERT {--} and compare it with different multi-lingual models on seven Romanian specific NLP tasks grouped into three categories, namely: sentiment analysis, dialect and cross-dialect topic identification, and diacritics restoration. Our model surpasses the multi-lingual models, as well as a another mono-lingual implementation of BERT, on all tasks."
}

@misc{masala2024vorbecstiromanecsterecipetrain,
      title={"Vorbe\c{s}ti Rom\^ane\c{s}te?" A Recipe to Train Powerful Romanian LLMs with English Instructions}, 
      author={Mihai Masala and Denis C. Ilie-Ablachim and Alexandru Dima and Dragos Corlatescu and Miruna Zavelca and Ovio Olaru and Simina Terian and Andrei Terian and Marius Leordeanu and Horia Velicu and Marius Popescu and Mihai Dascalu and Traian Rebedea},
      year={2024},
      eprint={2406.18266},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2406.18266}, 
}

@article{bruchAnalysisFusionFunctions2024,
  title = {An {{Analysis}} of {{Fusion Functions}} for {{Hybrid Retrieval}}},
  author = {Bruch, Sebastian and Gai, Siyu and Ingber, Amir},
  year = {2024},
  month = jan,
  journal = {ACM Transactions on Information Systems},
  volume = {42},
  number = {1},
  pages = {1--35},
  issn = {1046-8188, 1558-2868},
  doi = {10.1145/3596512},
  urldate = {2024-12-17},
  langid = {english}
}

@article{camposMSMARCOHuman2016,
  title = {{{MS MARCO}}: {{A Human Generated MAchine Reading COmprehension Dataset}}},
  shorttitle = {{{MS MARCO}}},
  author = {Campos, Daniel Fernando and Nguyen, Tri and Rosenberg, Mir and Song, Xia and Gao, Jianfeng and Tiwary, Saurabh and Majumder, Rangan and Deng, L. and Mitra, Bhaskar},
  year = {2016},
  month = nov,
  journal = {ArXiv},
  urldate = {2024-12-16}
}

@inproceedings{cerSemEval2017Task12017,
  title = {{{SemEval-2017 Task}} 1: {{Semantic Textual Similarity Multilingual}} and {{Crosslingual Focused Evaluation}}},
  shorttitle = {{{SemEval-2017 Task}} 1},
  booktitle = {Proceedings of the 11th {{International Workshop}} on {{Semantic Evaluation}}           ({{SemEval-2017}})},
  author = {Cer, Daniel and Diab, Mona and Agirre, Eneko and {Lopez-Gazpio}, Inigo and Specia, Lucia},
  year = {2017},
  pages = {1--14},
  publisher = {Association for Computational Linguistics},
  address = {Vancouver, Canada},
  doi = {10.18653/v1/S17-2001},
  urldate = {2024-12-15},
  langid = {english}
}

@misc{chenSPANNHighlyefficientBillionscale2021,
  title = {{{SPANN}}: {{Highly-efficient Billion-scale Approximate Nearest Neighbor Search}}},
  shorttitle = {{{SPANN}}},
  author = {Chen, Qi and Zhao, Bing and Wang, Haidong and Li, Mingqin and Liu, Chuanjie and Li, Zengzhong and Yang, Mao and Wang, Jingdong},
  year = {2021},
  month = nov,
  number = {arXiv:2111.08566},
  eprint = {2111.08566},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2111.08566},
  urldate = {2024-11-23},
  archiveprefix = {arXiv}
}

@inproceedings{cormackReciprocalRankFusion2009,
  title = {Reciprocal Rank Fusion Outperforms Condorcet and Individual Rank Learning Methods},
  booktitle = {Proceedings of the 32nd International {{ACM SIGIR}} Conference on {{Research}} and Development in Information Retrieval},
  author = {Cormack, Gordon V. and Clarke, Charles L A and Buettcher, Stefan},
  year = {2009},
  month = jul,
  pages = {758--759},
  publisher = {ACM},
  address = {Boston MA USA},
  doi = {10.1145/1571941.1572114},
  urldate = {2024-12-16},
  isbn = {978-1-60558-483-6},
  langid = {english}
}

@inproceedings{culottaConfidenceEstimationInformation2004,
  title = {Confidence Estimation for Information Extraction},
  booktitle = {Proceedings of {{HLT-NAACL}} 2004: {{Short Papers}} on {{XX}} - {{HLT-NAACL}} '04},
  author = {Culotta, Aron and McCallum, Andrew},
  year = {2004},
  pages = {109--112},
  publisher = {Association for Computational Linguistics},
  address = {Boston, Massachusetts},
  doi = {10.3115/1613984.1614012},
  urldate = {2025-01-29},
  isbn = {978-1-932432-24-4},
  langid = {english}
}

@misc{deepseek-aiDeepSeekR1IncentivizingReasoning2025,
  title = {{{DeepSeek-R1}}: {{Incentivizing Reasoning Capability}} in {{LLMs}} via {{Reinforcement Learning}}},
  shorttitle = {{{DeepSeek-R1}}},
  author = {{DeepSeek-AI} and Guo, Daya and Yang, Dejian and Zhang, Haowei and Song, Junxiao and Zhang, Ruoyu and Xu, Runxin and Zhu, Qihao and Ma, Shirong and Wang, Peiyi and Bi, Xiao and Zhang, Xiaokang and Yu, Xingkai and Wu, Yu and Wu, Z. F. and Gou, Zhibin and Shao, Zhihong and Li, Zhuoshu and Gao, Ziyi and Liu, Aixin and Xue, Bing and Wang, Bingxuan and Wu, Bochao and Feng, Bei and Lu, Chengda and Zhao, Chenggang and Deng, Chengqi and Zhang, Chenyu and Ruan, Chong and Dai, Damai and Chen, Deli and Ji, Dongjie and Li, Erhang and Lin, Fangyun and Dai, Fucong and Luo, Fuli and Hao, Guangbo and Chen, Guanting and Li, Guowei and Zhang, H. and Bao, Han and Xu, Hanwei and Wang, Haocheng and Ding, Honghui and Xin, Huajian and Gao, Huazuo and Qu, Hui and Li, Hui and Guo, Jianzhong and Li, Jiashi and Wang, Jiawei and Chen, Jingchang and Yuan, Jingyang and Qiu, Junjie and Li, Junlong and Cai, J. L. and Ni, Jiaqi and Liang, Jian and Chen, Jin and Dong, Kai and Hu, Kai and Gao, Kaige and Guan, Kang and Huang, Kexin and Yu, Kuai and Wang, Lean and Zhang, Lecong and Zhao, Liang and Wang, Litong and Zhang, Liyue and Xu, Lei and Xia, Leyi and Zhang, Mingchuan and Zhang, Minghua and Tang, Minghui and Li, Meng and Wang, Miaojun and Li, Mingming and Tian, Ning and Huang, Panpan and Zhang, Peng and Wang, Qiancheng and Chen, Qinyu and Du, Qiushi and Ge, Ruiqi and Zhang, Ruisong and Pan, Ruizhe and Wang, Runji and Chen, R. J. and Jin, R. L. and Chen, Ruyi and Lu, Shanghao and Zhou, Shangyan and Chen, Shanhuang and Ye, Shengfeng and Wang, Shiyu and Yu, Shuiping and Zhou, Shunfeng and Pan, Shuting and Li, S. S. and Zhou, Shuang and Wu, Shaoqing and Ye, Shengfeng and Yun, Tao and Pei, Tian and Sun, Tianyu and Wang, T. and Zeng, Wangding and Zhao, Wanjia and Liu, Wen and Liang, Wenfeng and Gao, Wenjun and Yu, Wenqin and Zhang, Wentao and Xiao, W. L. and An, Wei and Liu, Xiaodong and Wang, Xiaohan and Chen, Xiaokang and Nie, Xiaotao and Cheng, Xin and Liu, Xin and Xie, Xin and Liu, Xingchao and Yang, Xinyu and Li, Xinyuan and Su, Xuecheng and Lin, Xuheng and Li, X. Q. and Jin, Xiangyue and Shen, Xiaojin and Chen, Xiaosha and Sun, Xiaowen and Wang, Xiaoxiang and Song, Xinnan and Zhou, Xinyi and Wang, Xianzu and Shan, Xinxia and Li, Y. K. and Wang, Y. Q. and Wei, Y. X. and Zhang, Yang and Xu, Yanhong and Li, Yao and Zhao, Yao and Sun, Yaofeng and Wang, Yaohui and Yu, Yi and Zhang, Yichao and Shi, Yifan and Xiong, Yiliang and He, Ying and Piao, Yishi and Wang, Yisong and Tan, Yixuan and Ma, Yiyang and Liu, Yiyuan and Guo, Yongqiang and Ou, Yuan and Wang, Yuduan and Gong, Yue and Zou, Yuheng and He, Yujia and Xiong, Yunfan and Luo, Yuxiang and You, Yuxiang and Liu, Yuxuan and Zhou, Yuyang and Zhu, Y. X. and Xu, Yanhong and Huang, Yanping and Li, Yaohui and Zheng, Yi and Zhu, Yuchen and Ma, Yunxian and Tang, Ying and Zha, Yukun and Yan, Yuting and Ren, Z. Z. and Ren, Zehui and Sha, Zhangli and Fu, Zhe and Xu, Zhean and Xie, Zhenda and Zhang, Zhengyan and Hao, Zhewen and Ma, Zhicheng and Yan, Zhigang and Wu, Zhiyu and Gu, Zihui and Zhu, Zijia and Liu, Zijun and Li, Zilin and Xie, Ziwei and Song, Ziyang and Pan, Zizheng and Huang, Zhen and Xu, Zhipeng and Zhang, Zhongyu and Zhang, Zhen},
  year = {2025},
  month = jan,
  number = {arXiv:2501.12948},
  eprint = {2501.12948},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2501.12948},
  urldate = {2025-02-06},
  archiveprefix = {arXiv}
}

@misc{devlinBERTPretrainingDeep2019,
   title={Bert: Pre-training of deep bidirectional transformers for language understanding},
  author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
  booktitle={Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers)},
  pages={4171--4186},
  year={2019}
}

@misc{ellenOverviewTREC20042004,
  title = {Overview of the {{TREC}} 2004 {{Robust Retrieval Track}}},
  author = {Ellen, Voorhees},
  year = {2004}
}

@article{formalSPLADESparseLexical2021,
  title = {{{SPLADE}}: {{Sparse Lexical}} and {{Expansion Model}} for {{First Stage Ranking}}},
  shorttitle = {{{SPLADE}}},
  author = {Formal, Thibault and Piwowarski, Benjamin and Clinchant, St{\'e}phane},
  year = {2021},
  month = jul,
  journal = {Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  pages = {2288--2292},
  publisher = {ACM},
  address = {Virtual Event Canada},
  doi = {10.1145/3404835.3463098},
  urldate = {2024-12-16},
  isbn = {9781450380379},
  langid = {english}
}

@article{gohLargeLanguageModel2024,
  title = {Large {{Language Model Influence}} on {{Diagnostic Reasoning}}: {{A Randomized Clinical Trial}}},
  shorttitle = {Large {{Language Model Influence}} on {{Diagnostic Reasoning}}},
  author = {Goh, Ethan and Gallo, Robert and Hom, Jason and Strong, Eric and Weng, Yingjie and Kerman, Hannah and Cool, Jos{\'e}phine A. and Kanjee, Zahir and Parsons, Andrew S. and Ahuja, Neera and Horvitz, Eric and Yang, Daniel and Milstein, Arnold and Olson, Andrew P. J. and Rodman, Adam and Chen, Jonathan H.},
  year = {2024},
  month = oct,
  journal = {JAMA Network Open},
  volume = {7},
  number = {10},
  pages = {e2440969},
  issn = {2574-3805},
  doi = {10.1001/jamanetworkopen.2024.40969},
  urldate = {2025-01-30},
  langid = {english}
}

@misc{HardProblemsThat2025,
  title = {Hard Problems That Reduce to Document Ranking},
  year = {2025},
  month = feb,
  urldate = {2025-03-18},
  chapter = {posts},
  howpublished = {https://noperator.dev/posts/document-ranking-for-complex-problems/},
  langid = {english}
}

@article{harperMovieLensDatasetsHistory2016,
  title = {The {{MovieLens Datasets}}: {{History}} and {{Context}}},
  shorttitle = {The {{MovieLens Datasets}}},
  author = {Harper, F. Maxwell and Konstan, Joseph A.},
  year = {2016},
  month = jan,
  journal = {ACM Transactions on Interactive Intelligent Systems},
  volume = {5},
  number = {4},
  pages = {1--19},
  issn = {2160-6455, 2160-6463},
  doi = {10.1145/2827872},
  urldate = {2024-12-18},
  langid = {english}
}

@article{hochreiterLongShortTermMemory1997,
  title = {Long {{Short-Term Memory}}},
  author = {Hochreiter, Sepp and Schmidhuber, J{\"u}rgen},
  year = {1997},
  month = nov,
  journal = {Neural Computation},
  volume = {9},
  number = {8},
  pages = {1735--1780},
  issn = {0899-7667},
  doi = {10.1162/neco.1997.9.8.1735},
  urldate = {2025-03-19}
}

@article{hoppeChatGPTGPT4Outperforms2024,
  title = {{{ChatGPT With GPT-4 Outperforms Emergency Department Physicians}} in {{Diagnostic Accuracy}}: {{Retrospective Analysis}}},
  shorttitle = {{{ChatGPT With GPT-4 Outperforms Emergency Department Physicians}} in {{Diagnostic Accuracy}}},
  author = {Hoppe, John Michael and Auer, Matthias K and Str{\"u}ven, Anna and Massberg, Steffen and Stremmel, Christopher},
  year = {2024},
  month = jul,
  journal = {Journal of Medical Internet Research},
  volume = {26},
  pages = {e56110},
  issn = {1438-8871},
  doi = {10.2196/56110},
  urldate = {2025-01-31},
  langid = {english}
}

@article{houLargeLanguageModels2023,
title={Large language models are zero-shot rankers for recommender systems},
  author={Hou, Yupeng and Zhang, Junjie and Lin, Zihan and Lu, Hongyu and Xie, Ruobing and McAuley, Julian and Zhao, Wayne Xin},
  booktitle={European Conference on Information Retrieval},
  pages={364--381},
  year={2024},
  organization={Springer}
}

@article{houLearningVectorQuantizedItem2023,
  title = {Learning {{Vector-Quantized Item Representation}} for {{Transferable Sequential Recommenders}}},
  author = {Hou, Yupeng and He, Zhankui and McAuley, Julian and Zhao, Wayne Xin},
  year = {2023},
  month = apr,
  journal = {Proceedings of the ACM Web Conference 2023},
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  publisher = {ACM},
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  doi = {10.1145/3543507.3583434},
  urldate = {2024-12-18},
  isbn = {9781450394161},
  langid = {english}
}

@article{houUniversalSequenceRepresentation2022,
  title = {Towards {{Universal Sequence Representation Learning}} for {{Recommender Systems}}},
  author = {Hou, Yupeng and Mu, Shanlei and Zhao, Wayne Xin and Li, Yaliang and Ding, Bolin and Wen, Ji-Rong},
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  month = aug,
  journal = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
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  urldate = {2024-12-18},
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  langid = {english}
}

@misc{huLoRALowRankAdaptation2021,
  title = {{{LoRA}}: {{Low-Rank Adaptation}} of {{Large Language Models}}},
  shorttitle = {{{LoRA}}},
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  eprint = {2106.09685},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2106.09685},
  urldate = {2024-12-18},
  archiveprefix = {arXiv}
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@misc{infiniflowSparseEmbeddingBM252024,
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  langid = {english}
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  title = {{{IR}} Evaluation Methods for Retrieving Highly Relevant Documents},
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  title = {{{ColBERT}}: {{Efficient}} and {{Effective Passage Search}} via {{Contextualized Late Interaction}} over {{BERT}}},
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@article{kimCanNaturalLanguage2023,
  title = {Can {{Natural Language Processing}} and {{Artificial Intelligence Automate The Generation}} of {{Billing Codes From Operative Note Dictations}}?},
  author = {Kim, Jun S. and Vivas, Andrew and Arvind, Varun and Lombardi, Joseph and Reidler, Jay and Zuckerman, Scott L and Lee, Nathan J. and Vulapalli, Meghana and Geng, Eric A and Cho, Brian H. and Morizane, Kazuaki and Cho, Samuel K. and Lehman, Ronald A. and Lenke, Lawrence G. and Riew, Kiehyun Daniel},
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  urldate = {2025-02-06},
  langid = {english}
}

@inproceedings{knyazevLightweightMethodModeling2023,
  title = {A {{Lightweight Method}} for {{Modeling Confidence}} in {{Recommendations}} with {{Learned Beta Distributions}}},
  booktitle = {Proceedings of the 17th {{ACM Conference}} on {{Recommender Systems}}},
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  langid = {english}
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@misc{leDistributedRepresentationsSentences2014a,
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@article{levyComparisonMachineLearningAlgorithms2022,
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  author = {Levy, Joshua and Vattikonda, Nishitha and Haudenschild, Christian and Christensen, Brock and Vaickus, Louis},
  year = {2022},
  journal = {Journal of Pathology Informatics},
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@article{linHowCanRecommender2023,
  title = {How {{Can Recommender Systems Benefit}} from {{Large Language Models}}: {{A Survey}}},
  shorttitle = {How {{Can Recommender Systems Benefit}} from {{Large Language Models}}},
  author = {Lin, Jianghao and Dai, Xinyi and Xi, Yunjia and Liu, Weiwen and Chen, Bo and Zhang, Hao and Liu, Yong and Wu, Chuhan and Li, Xiangyang and Zhu, Chenxu and Guo, Huifeng and Yu, Yong and Tang, Ruiming and Zhang, Weinan},
  year = {2023},
  publisher = {arXiv},
  doi = {10.48550/ARXIV.2306.05817},
  urldate = {2024-12-15},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@article{maLargeLanguageModel2023,
  title = {Large {{Language Model Is Not}} a {{Good Few-shot Information Extractor}}, but a {{Good Reranker}} for {{Hard Samples}}!},
  author = {Ma, Yubo and Cao, Yixin and Hong, Yong and Sun, Aixin},
  year = {2023},
  journal = {Findings of the Association for Computational Linguistics: EMNLP 2023},
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  publisher = {Association for Computational Linguistics},
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  urldate = {2024-12-18},
  langid = {english}
}

@misc{MetallamaLlama3370BInstructHugging2024,
  title = {Meta-Llama/{{Llama-3}}.3-{{70B-Instruct}} {$\cdot$} {{Hugging Face}}},
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  month = dec,
  urldate = {2025-02-06},
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}

@misc{MetallamaMetaLlama370BInstructHugging2024,
  title = {Meta-Llama/{{Meta-Llama-3-70B-Instruct}} {$\cdot$} {{Hugging Face}}},
  year = {2024},
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  urldate = {2025-02-03},
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@misc{MilvusHighPerformanceVector,
  title = {Milvus {\textbar} {{High-Performance Vector Database Built}} for {{Scale}}},
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  langid = {english}
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@misc{MTEBLeaderboardHugging,
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@misc{muennighoffMTEBMassiveText2023,
  title = {{{MTEB}}: {{Massive Text Embedding Benchmark}}},
  shorttitle = {{{MTEB}}},
  author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"i}c and Reimers, Nils},
  year = {2023},
  month = mar,
  number = {arXiv:2210.07316},
  eprint = {2210.07316},
  primaryclass = {cs},
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  urldate = {2024-12-15},
  archiveprefix = {arXiv},
  langid = {english}
}

@article{naziLargeLanguageModels2024,
  title = {Large {{Language Models}} in {{Healthcare}} and {{Medical Domain}}: {{A Review}}},
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  year = {2024},
  month = sep,
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  copyright = {http://creativecommons.org/licenses/by/3.0/},
  langid = {english}
}

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@inproceedings{oosterhuisReliableConfidenceIntervals2024,
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@article{tangEvaluatingLargeLanguage2023,
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@article{tavabiSystematicEvaluationCommon2024,
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@misc{turnbullRRFNotEnough,
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@misc{UnderstandingBM25Full2024,
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@misc{vaswaniAttentionAllYou2023,
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@misc{wangMultilingualE5Text2024,
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  archiveprefix = {arXiv},
  langid = {english}
}

@misc{wangTextEmbeddingsWeaklySupervised2024,
  title = {Text {{Embeddings}} by {{Weakly-Supervised Contrastive Pre-training}}},
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@article{zaidatCanNovelNatural2024,
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@inproceedings{wangMilvusPurposeBuiltVector2021,
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