---
title: "Greater Boston — works of 2023: 41 papers, the most cited first"
description: "41 works of Greater Boston from 2023 in our library, the most cited first. Among them: Performance of ChatGPT on USMLE: Potential for…"
canonical: "https://research.elamur.ai/free/en/agglomerations/greater-boston/year/2023/"
---
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# Works of Greater Boston from 2023

41 works · the most cited first · [← Greater Boston](https://md.research.elamur.ai/free/en/agglomerations/greater-boston/)

1. [**Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models PLOS Digital Health**Tiffany H. Kung, Morgan Cheatham, Arielle Medenilla, et al.*2023 · 4,168 citations*](https://md.research.elamur.ai/free/en/papers/kung-2023-performance-chatgpt-usmle-potential-ai/)
2. **[MIMIC-IV, a freely accessible electronic health record dataset](https://md.research.elamur.ai/free/en/papers/johnson-2023-mimic-iv-freely-accessible-electronic/) [Scientific Data](https://md.research.elamur.ai/free/en/venues/scientific-data/)**Alistair E. W. Johnson, Lucas Bulgarelli, Lu Shen, et al.*2023 · 3,386 citations*
3. **[ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://md.research.elamur.ai/free/en/papers/gilardi-2023-chatgpt-outperforms-crowd-workers-text/) [Proceedings of the National Academy of Sciences](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-national-academy-of-sciences/)**Fabrizio Gilardi, Meysam Alizadeh, Maël Kubli*2023 · 1,533 citations*
4. [**Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models arXiv:2304.01852 · Meta-Radiology (2023)100,017**Yiheng Liu, Tianle Han, Siyuan Ma, et al.*2023 · 938 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-summary-chatgpt-related-research-perspective/)
5. [**Improving Factuality and Reasoning in Language Models through Multiagent Debate arXiv:2305.14325**Yilun Du, Shuang Li, Antonio Torralba, et al.*2023 · 565 citations*](https://md.research.elamur.ai/free/en/papers/du-2023-improving-factuality-reasoning-language-models/)
6. [**LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models arXiv:2308.11462**Neel Guha; Julian Nyarko; Daniel E. Ho; Christopher Re; Adam Chilton; Aditya Narayana; Alex Chohlas-Wood; Austin Peters; Brandon Waldon; Daniel N. Rockmore; Diego Zambrano; Dmitry Talisman; Enam Hoque; Faiz Surani; Frank Fagan; Galit Sarfaty; Gregory M. Dickinson; Haggai Porat; Jason Hegland; Jessica Wu; Joe Nudell; Joel Niklaus; John Nay; Jonathan H. Choi; Kevin Tobia; Margaret Hagan; Megan Ma; Michael Livermore; Nikon Rasumov-Rahe; Nils Holzenberger; Noam Kolt; Peter Henderson; Sean Rehaag; Sharad Goel; Shang Gao; Spencer Williams; Sunny Gandhi; Tom Zur; Varun Iyer;, Zehua Li*2023 · 381 citations*](https://md.research.elamur.ai/free/en/papers/guha-2023-legalbench-collaboratively-built-benchmark-measuring/)
7. **[AugGPT: Leveraging ChatGPT for Text Data Augmentation](https://md.research.elamur.ai/free/en/papers/dai-2023-auggpt-leveraging-chatgpt-text-data/) [IEEE Transactions on Big Data](https://md.research.elamur.ai/free/en/venues/ieee-transactions-on-big-data/)**Haixing Dai; Zhengliang Liu; Wenxiong Liao; Xiaoke Huang; Yihan Cao; Zihao Wu; Lin Zhao; Shaochen Xu; Wei Liu; Ninghao Liu; Sheng Li; Dajiang Zhu; Hongmin Cai; Lichao Sun; Quanzheng Li; Dinggang Shen; Tianming Liu;, Xiang Li*2023 · 306 citations*
8. [**From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference arXiv:2310.03003**Siddharth Samsi, Dan Zhao, Joseph McDonald, et al.*2023 · 288 citations*](https://md.research.elamur.ai/free/en/papers/samsi-2023-words-watts-benchmarking-energy-costs/)
9. [**Larger language models do in-context learning differently arXiv:2303.03846**Jerry Wei, Jason Wei, Yi Tay, et al.*2023 · 279 citations*](https://md.research.elamur.ai/free/en/papers/wei-2023-larger-language-models-context-learning/)
10. [**LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models arXiv:2309.12307**Yukang Chen, Shengju Qian, Haotian Tang, et al.*2023 · 265 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-longlora-efficient-fine-tuning-long/)
11. [**DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models arXiv:2309.03883**Yung-Sung Chuang, Yujia Xie, Hongyin Luo, et al.*2023 · 261 citations*](https://md.research.elamur.ai/free/en/papers/chuang-2023-dola-decoding-contrasting-layers-improves/)
12. **[LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers](https://md.research.elamur.ai/free/en/papers/olausson-2023-linc-neurosymbolic-approach-logical-reasoning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Theo X. Olausson, Alex Gu, Benjamin Lipkin, et al.*2023 · 171 citations*
13. [**Low-Resource Languages Jailbreak GPT-4 arXiv:2310.02446**Zheng-Xin Yong, Cristina Menghini, Stephen H. Bach*2023 · 168 citations*](https://md.research.elamur.ai/free/en/papers/yong-2023-low-resource-languages-jailbreak-gpt/)
14. [**The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets arXiv:2310.06824**Samuel Marks, Max Tegmark*2023 · 160 citations*](https://md.research.elamur.ai/free/en/papers/marks-2023-geometry-truth-emergent-linear-structure/)
15. **[Testing the Predictions of Surprisal Theory in 11 Languages](https://md.research.elamur.ai/free/en/papers/wilcox-2023-testing-predictions-surprisal-theory-11/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Ethan G. Wilcox, Tiago Pimentel, Clara Meister, et al.*2023 · 158 citations*
16. [**The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset arXiv:2303.03915**Hugo Laurençon, Lucile Saulnier, Thomas Wang, et al.*2023 · 147 citations*](https://md.research.elamur.ai/free/en/papers/lauren-ccon-2023-bigscience-roots-corpus-1-6tb/)
17. **[Efficient Methods for Natural Language Processing: A Survey](https://md.research.elamur.ai/free/en/papers/treviso-2023-efficient-methods-natural-language-processing/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Marcos Treviso, Ji-Ung Lee, Tianchu Ji, et al.*2023 · 145 citations*
18. [**A fine-grained comparison of pragmatic language understanding in humans and language models**Jennifer Hu, Sammy Floyd, Olessia Jouravlev, et al.*2023 · 136 citations*](https://md.research.elamur.ai/free/en/papers/hu-2023-fine-grained-comparison-pragmatic-language/)
19. **[Prompting is not a substitute for probability measurements in large language models](https://md.research.elamur.ai/free/en/papers/hu-2023-prompting-is-not-substitute-probability/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jennifer Hu, Roger Levy*2023 · 133 citations*
20. [**Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning arXiv:2303.15647**Vladislav Lialin, Vijeta Deshpande, Xiaowei Yao, et al.*2023 · 128 citations*](https://md.research.elamur.ai/free/en/papers/lialin-2023-scaling-down-scale-up-guide/)
21. **[Machine Learning for Ancient Languages: A Survey](https://md.research.elamur.ai/free/en/papers/sommerschield-2023-machine-learning-ancient-languages-survey/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Thea Sommerschield, Yannis Assael, John Pavlopoulos, et al.*2023 · 123 citations*
22. [**Pretraining Language Models with Human Preferences arXiv:2302.08582**Tomasz Korbak, Kejian Shi, Angelica Chen, et al.*2023 · 118 citations*](https://md.research.elamur.ai/free/en/papers/korbak-2023-pretraining-language-models-human-preferences/)
23. [**Certifying LLM Safety against Adversarial Prompting arXiv:2309.02705**Aounon Kumar, Chirag Agarwal, Suraj Srinivas, et al.*2023 · 110 citations*](https://md.research.elamur.ai/free/en/papers/kumar-2023-certifying-llm-safety-against-adversarial/)
24. [**Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification arXiv:2308.07921**Aojun Zhou, Ke Wang, Zimu Lu, et al.*2023 · 109 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-solving-challenging-math-word-problems/)
25. **[We're Afraid Language Models Aren't Modeling Ambiguity](https://md.research.elamur.ai/free/en/papers/liu-2023-we-re-afraid-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Alisa Liu, Zhaofeng Wu, Julian Michael, et al.*2023 · 108 citations*
26. **[BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting](https://md.research.elamur.ai/free/en/papers/yong-2023-bloom-1-adding-language-support/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zheng Xin Yong, Hailey Schoelkopf, Niklas Muennighoff, et al.*2023 · 101 citations*
27. [**Inverse Scaling: When Bigger Isn't Better arXiv:2306.09479 · Transactions on Machine Learning Research (TMLR), 10/2023, https://openreview.net/forum?id=DwgRm72GQF**Ian R. McKenzie, Alexander Lyzhov, Michael Pieler, et al.*2023 · 99 citations*](https://md.research.elamur.ai/free/en/papers/mckenzie-2023-inverse-scaling-bigger-isn-t/)
28. [**A Survey of Large Language Models in Medicine: Progress, Application, and Challenge arXiv:2311.05112**Hongjian Zhou, Fenglin Liu, Boyang Gu, et al.*2023 · 87 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-survey-large-language-models-medicine/)
29. **[RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputs](https://md.research.elamur.ai/free/en/papers/akyurek-2023-rl4f-generating-natural-language-feedback/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Afra Feyza Akyurek, Ekin Akyurek, Ashwin Kalyan, et al.*2023 · 87 citations*
30. **[Multilingual Large Language Models Are Not (Yet) Code-Switchers](https://md.research.elamur.ai/free/en/papers/zhang-2023-multilingual-large-language-models-are/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ruochen Zhang, Samuel Cahyawijaya, Jan Christian Blaise Cruz, et al.*2023 · 85 citations*
31. [**ReLoRA: High-Rank Training Through Low-Rank Updates arXiv:2307.05695**Vladislav Lialin, Namrata Shivagunde, Sherin Muckatira, et al.*2023 · 74 citations*](https://md.research.elamur.ai/free/en/papers/lialin-2023-relora-high-rank-training-through/)
32. [**Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models arXiv:2305.14705**Sheng Shen, Le Hou, Yanqi Zhou, et al.*2023 · 69 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-mixture-experts-meets-instruction-tuning/)
33. [**Large Language Model Routing with Benchmark Datasets arXiv:2309.15789**Tal Shnitzer, Anthony Ou, Mirian Silva, et al.*2023 · 61 citations*](https://md.research.elamur.ai/free/en/papers/shnitzer-2023-large-language-model-routing-benchmark/)
34. **[How poor is the stimulus? Evaluating hierarchical generalization in neural networks trained on child-directed speech](https://md.research.elamur.ai/free/en/papers/yedetore-2023-how-poor-is-stimulus-evaluating/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Aditya Yedetore, Tal Linzen, Robert Frank, et al.*2023 · 48 citations*
35. **[Findings of the Second WMT Shared Task on Sign Language Translation (WMT-SLT23)](https://md.research.elamur.ai/free/en/papers/muller-2023-findings-second-wmt-shared-task/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Mathias Müller, Malihe Alikhani, Eleftherios Avramidis, et al.*2023 · 39 citations*
36. **[Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches](https://md.research.elamur.ai/free/en/papers/fried-2023-pragmatics-language-grounding-phenomena-tasks/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Daniel Fried, Nicholas Tomlin, Jennifer Hu, et al.*2023 · 36 citations*
37. [**Expectations over Unspoken Alternatives Predict Pragmatic Inferences**Jennifer Hu, Roger Levy, Judith Degen, et al.*2023 · 27 citations*](https://md.research.elamur.ai/free/en/papers/hu-2023-expectations-over-unspoken-alternatives-predict/)
38. **[What happens before and after: Multi-Event Commonsense in Event Coreference Resolution](https://md.research.elamur.ai/free/en/papers/ravi-2023-what-happens-before-after-multi/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Sahithya Ravi, Chris Tanner, Raymond Ng, et al.*2023 · 21 citations*
39. [**DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules**Yanchen Liu, William Held, Diyi Yang*2023 · 20 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-dada-dialect-adaptation-dynamic-aggregation/)
40. [**Evaluating statistical language models as pragmatic reasoners arXiv:2305.01020**Benjamin Lipkin, Lionel Wong, Gabriel Grand, et al.*2023 · 18 citations*](https://md.research.elamur.ai/free/en/papers/lipkin-2023-evaluating-statistical-language-models-pragmatic/)
41. [**Data-driven Parsing Evaluation for Child-Parent Interactions**Zoey Liu, Emily Prud'hommeaux*2023 · 8 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-data-driven-parsing-evaluation-child/)
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