---
title: "Who cites Ryan Teehan — page 4 — 2,175 papers of our library"
description: "2,175 papers of the Elamur Research library cite the works of Ryan Teehan, found in their bibliographies · page 4 of 7 · 301–400; the most cited first."
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---
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# Who cites Ryan Teehan

2,294 citations of their works from 2,175 papers of our library (of the 149,439 whose bibliographies we read) · 628 with a page here, the most cited first · page 4 of 7 · 301–400 · [← Ryan Teehan](https://md.research.elamur.ai/free/en/authors/ryan-teehan/)

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301. **[SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoning](https://md.research.elamur.ai/free/en/papers/wang-2024-seaeval-multilingual-foundation-models-cross/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Bin Wang, Zhengyuan Liu, Xin Huang, et al.*2024 · 85 citations*
302. [**Investigating the Translation Performance of a Large Multilingual Language Model: the Case of BLOOM arXiv:2303.01911**Rachel Bawden, François Yvon*2023 · 85 citations*](https://md.research.elamur.ai/free/en/papers/bawden-2023-investigating-translation-performance-large-multilingual/)
303. [**AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model arXiv:2208.01448**Saleh Soltan, Shankar Ananthakrishnan, Jack FitzGerald, et al.*2022 · 84 citations*](https://md.research.elamur.ai/free/en/papers/soltan-2022-alexatm-20b-few-shot-learning/)
304. **[Mitigating the Alignment Tax of RLHF](https://md.research.elamur.ai/free/en/papers/lin-2024-mitigating-alignment-tax-rlhf/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yong Lin, Hangyu Lin, Wei Xiong, et al.*2024 · 83 citations*
305. **[DOC: Improving Long Story Coherence With Detailed Outline Control](https://md.research.elamur.ai/free/en/papers/yang-2023-doc-improving-long-story-coherence/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kevin Yang, Dan Klein, Nanyun Peng, et al.*2023 · 83 citations*
306. **[ORCA: A Challenging Benchmark for Arabic Language Understanding](https://md.research.elamur.ai/free/en/papers/elmadany-2023-orca-challenging-benchmark-arabic-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**AbdelRahim Elmadany, ElMoatez Billah Nagoudi, Muhammad Abdul-Mageed*2023 · 83 citations*
307. [**Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks arXiv:2310.10844**Erfan Shayegani, Md Abdullah Al Mamun, Yu Fu, et al.*2023 · 83 citations*](https://md.research.elamur.ai/free/en/papers/shayegani-2023-survey-vulnerabilities-large-language-models/)
308. [**Knowledge Fusion of Large Language Models arXiv:2401.10491**Fanqi Wan, Xinting Huang, Deng Cai, et al.*2024 · 82 citations*](https://md.research.elamur.ai/free/en/papers/wan-2024-knowledge-fusion-large-language-models/)
309. [**Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive arXiv:2402.13228**Arka Pal, Deep Karkhanis, Samuel Dooley, et al.*2024 · 82 citations*](https://md.research.elamur.ai/free/en/papers/pal-2024-smaug-fixing-failure-modes-preference/)
310. **[Style Over Substance: Evaluation Biases for Large Language Models](https://md.research.elamur.ai/free/en/papers/wu-2025-style-over-substance-evaluation-biases/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Minghao Wu, Alham Fikri Aji*2025 · 81 citations*
311. **[You Only Look at Screens: Multimodal Chain-of-Action Agents](https://md.research.elamur.ai/free/en/papers/zhang-2024-you-only-look-screens-multimodal/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhuosheng Zhang, Aston Zhang*2024 · 81 citations*
312. **[MasakhaNEWS: News Topic Classification for African languages](https://md.research.elamur.ai/free/en/papers/adelani-2023-masakhanews-news-topic-classification-african/) [International Joint Conference on Natural Language Processing (IJCNLP)](https://md.research.elamur.ai/free/en/venues/ijcnlp/)**David Ifeoluwa Adelani, Marek Masiak, Israel Abebe Azime, et al.*2023 · 81 citations*
313. **[AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents](https://md.research.elamur.ai/free/en/papers/trivedi-2024-appworld-controllable-world-apps-people/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Harsh Trivedi, Tushar Khot, Mareike Hartmann, et al.*2024 · 80 citations*
314. **[KoBBQ: Korean Bias Benchmark for Question Answering](https://md.research.elamur.ai/free/en/papers/jin-2024-kobbq-korean-bias-benchmark-question/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Jiho Jin, Jiseon Kim, Nayeon Lee, et al.*2024 · 78 citations*
315. **[Multilingual Instruction Tuning With Just a Pinch of Multilinguality](https://md.research.elamur.ai/free/en/papers/shaham-2024-multilingual-instruction-tuning-just-pinch/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Uri Shaham, Jonathan Herzig, Roee Aharoni, et al.*2024 · 78 citations*
316. **[KMMLU: Measuring Massive Multitask Language Understanding in Korean](https://md.research.elamur.ai/free/en/papers/son-2025-kmmlu-measuring-massive-multitask-language/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Guijin Son, Hanwool Lee, Sungdong Kim, et al.*2025 · 76 citations*
317. **[Domain Adaptation with Pre-trained Transformers for Query-Focused Abstractive Text Summarization](https://md.research.elamur.ai/free/en/papers/laskar-2022-domain-adaptation-pre-trained-transformers/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Md Tahmid Rahman Laskar, Enamul Hoque, Jimmy Xiangji Huang*2022 · 76 citations*
318. [**PUB: A Pragmatics Understanding Benchmark for Assessing LLMs' Pragmatics Capabilities**Settaluri Sravanthi, Meet Doshi, Pavan Tankala, et al.*2024 · 75 citations*](https://md.research.elamur.ai/free/en/papers/sravanthi-2024-pub-pragmatics-understanding-benchmark-assessing/)
319. **[SciCode: A Research Coding Benchmark Curated by Scientists](https://md.research.elamur.ai/free/en/papers/tian-2024-scicode-research-coding-benchmark/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Minyang Tian, Luyu Gao, Shizhuo Dylan Zhang, et al.*2024 · 75 citations*
320. [**Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning arXiv:2305.19523**Xiaoxin He, Xavier Bresson, Thomas Laurent, et al.*2023 · 75 citations*](https://md.research.elamur.ai/free/en/papers/he-2023-harnessing-explanations-llm-lm-interpreter/)
321. [**Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration arXiv:2306.09093**Chenyang Lyu, Minghao Wu, Longyue Wang, et al.*2023 · 75 citations*](https://md.research.elamur.ai/free/en/papers/lyu-2023-macaw-llm-multi-modal-language/)
322. **[Training Language Models to Reason Efficiently](https://md.research.elamur.ai/free/en/papers/arora-2025-training-language-models-reason-efficiently/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Daman Arora, Andrea Zanette*2025 · 74 citations*
323. [**INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge arXiv:2411.19799**Angelika Romanou, Negar Foroutan, Anna Sotnikova, et al.*2024 · 74 citations*](https://md.research.elamur.ai/free/en/papers/romanou-2024-include-evaluating-multilingual-language-understanding/)
324. **[MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoning](https://md.research.elamur.ai/free/en/papers/li-2024-mediq-question-asking-llms-benchmark/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shuyue Stella Li, Vidhisha Balachandran, Shangbin Feng, et al.*2024 · 74 citations*
325. **[MEGAVERSE: Benchmarking Large Language Models Across Languages, Modalities, Models and Tasks](https://md.research.elamur.ai/free/en/papers/ahuja-2024-megaverse-benchmarking-large-language-models/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Sanchit Ahuja, Divyanshu Aggarwal, Varun Gumma, et al.*2024 · 74 citations*
326. [**When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards arXiv:2402.01781**Norah Alzahrani, Hisham Abdullah Alyahya, Yazeed Alnumay, et al.*2024 · 74 citations*](https://md.research.elamur.ai/free/en/papers/alzahrani-2024-benchmarks-targets-revealing-sensitivity-large/)
327. [**AlignBench: Benchmarking Chinese Alignment of Large Language Models arXiv:2311.18743**Xiao Liu, Xuanyu Lei, Shengyuan Wang, et al.*2023 · 74 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-alignbench-benchmarking-chinese-alignment/)
328. [**Large Language Models as Analogical Reasoners arXiv:2310.01714**Michihiro Yasunaga, Xinyun Chen, Yujia Li, et al.*2023 · 74 citations*](https://md.research.elamur.ai/free/en/papers/yasunaga-2023-large-language-models-analogical-reasoners/)
329. [**Benchmarking Benchmark Leakage in Large Language Models arXiv:2404.18824**Ruijie Xu, Zengzhi Wang, Run-Ze Fan, et al.*2024 · 73 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-benchmarking-benchmark-leakage-large-language/)
330. [**What Language Model to Train if You Have One Million GPU Hours? arXiv:2210.15424**Teven Le Scao, Thomas Wang, Daniel Hesslow, et al.*2022 · 73 citations*](https://md.research.elamur.ai/free/en/papers/scao-2022-language-model-train-if-have/)
331. [**InPars: Data Augmentation for Information Retrieval using Large Language Models arXiv:2202.05144**Luiz Bonifacio, Hugo Abonizio, Marzieh Fadaee, et al.*2022 · 72 citations*](https://md.research.elamur.ai/free/en/papers/bonifacio-2022-inpars-data-augmentation-information-retrieval/)
332. **[Lion: Adversarial Distillation of Proprietary Large Language Models](https://md.research.elamur.ai/free/en/papers/jiang-2023-lion-adversarial-distillation-proprietary-large/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yuxin Jiang, Chunkit Chan, Mingyang Chen, et al.*2023 · 70 citations*
333. [**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 · 70 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-mixture-experts-meets-instruction-tuning/)
334. **[Automatic Generation of Socratic Subquestions for Teaching Math Word Problems](https://md.research.elamur.ai/free/en/papers/shridhar-2022-automatic-generation-socratic-subquestions-teaching/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kumar Shridhar, Jakub Macina, Mennatallah El-Assady, et al.*2022 · 70 citations*
335. **[Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation](https://md.research.elamur.ai/free/en/papers/zhang-2024-self-alignment-factuality-mitigating-hallucinations/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaoying Zhang, Baolin Peng, Ye Tian, et al.*2024 · 69 citations*
336. [**Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning arXiv:2309.05444**Ted Zadouri, Ahmet Ustun, Arash Ahmadian, et al.*2023 · 69 citations*](https://md.research.elamur.ai/free/en/papers/zadouri-2023-pushing-mixture-experts-limit-extremely/)
337. **[A Thorough Examination of Decoding Methods in the Era of LLMs](https://md.research.elamur.ai/free/en/papers/shi-2024-thorough-examination-decoding-methods-era/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Chufan Shi, Haoran Yang, Deng Cai, et al.*2024 · 68 citations*
338. **[ThoughtSource: A central hub for large language model reasoning data](https://md.research.elamur.ai/free/en/papers/ott-2023-thoughtsource-central-hub-large-language/) [Scientific Data](https://md.research.elamur.ai/free/en/venues/scientific-data/)**Simon Ott, Konstantin Hebenstreit, Valentin Lievin, et al.*2023 · 68 citations*
339. **[Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models](https://md.research.elamur.ai/free/en/papers/yang-2024-buffer-thoughts-thought-augmented-reasoning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Ling Yang, Zhaochen Yu, Tianjun Zhang, et al.*2024 · 67 citations*
340. **[Do Language Models Have a Common Sense regarding Time? Revisiting Temporal Commonsense Reasoning in the Era of Large Language Models](https://md.research.elamur.ai/free/en/papers/jain-2023-do-language-models-have-common/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Raghav Jain, Daivik Sojitra, Arkadeep Acharya, et al.*2023 · 67 citations*
341. [**Learning by Distilling Context arXiv:2209.15189**Charlie Snell, Dan Klein, Ruiqi Zhong*2022 · 67 citations*](https://md.research.elamur.ai/free/en/papers/snell-2022-learning-distilling-context/)
342. **[Knowledge of Knowledge: Exploring Known-Unknowns Uncertainty with Large Language Models](https://md.research.elamur.ai/free/en/papers/amayuelas-2024-knowledge-knowledge-exploring-known-unknowns/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Alfonso Amayuelas, Kyle Wong, Liangming Pan, et al.*2024 · 66 citations*
343. [**An Open Source Data Contamination Report for Large Language Models arXiv:2310.17589**Yucheng Li, Frank Guerin, Chenghua Lin*2023 · 66 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-open-source-data-contamination-report/)
344. [**ARB: Advanced Reasoning Benchmark for Large Language Models arXiv:2307.13692**Tomohiro Sawada, Daniel Paleka, Alexander Havrilla, et al.*2023 · 66 citations*](https://md.research.elamur.ai/free/en/papers/sawada-2023-arb-advanced-reasoning-benchmark-large/)
345. **[MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/chang-2025-main-rag-multi-agent-filtering/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chia-Yuan Chang, Zhimeng Jiang, Vineeth Rakesh, et al.*2025 · 65 citations*
346. [**Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models arXiv:2402.13064**Haoran Li, Qingxiu Dong, Zhengyang Tang, et al.*2024 · 65 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-synthetic-data-almost-scratch-generalized/)
347. [**Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks arXiv:2305.14201**Tiedong Liu, Bryan Kian Hsiang Low*2023 · 65 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-goat-fine-tuned-llama-outperforms/)
348. **[Questions Are All You Need to Train a Dense Passage Retriever](https://md.research.elamur.ai/free/en/papers/sachan-2023-questions-are-all-you-need/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Devendra Singh Sachan, Mike Lewis, Dani Yogatama, et al.*2023 · 65 citations*
349. **[Why think step by step? Reasoning emerges from the locality of experience](https://md.research.elamur.ai/free/en/papers/prystawski-2023-why-think-step-step-reasoning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Ben Prystawski, Michael Y. Li, Noah D. Goodman*2023 · 65 citations*
350. **[Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge](https://md.research.elamur.ai/free/en/papers/wu-2025-meta-rewarding-language-models-self/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tianhao Wu, Weizhe Yuan, Olga Golovneva, et al.*2025 · 64 citations*
351. **[A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners](https://md.research.elamur.ai/free/en/papers/jiang-2024-peek-into-token-bias-large/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Bowen Jiang, Yangxinyu Xie, Zhuoqun Hao, et al.*2024 · 64 citations*
352. **[Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2024-agent-flan-designing-data-methods/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zehui Chen, Kuikun Liu, Qiuchen Wang, et al.*2024 · 64 citations*
353. [**CValues: Measuring the Values of Chinese Large Language Models from Safety to Responsibility arXiv:2307.09705**Guohai Xu, Jiayi Liu, Ming Yan, et al.*2023 · 64 citations*](https://md.research.elamur.ai/free/en/papers/xu-2023-cvalues-measuring-values-chinese/)
354. **[Leveraging Large Language Models for NLG Evaluation: Advances and Challenges](https://md.research.elamur.ai/free/en/papers/li-2024-leveraging-large-language-models-nlg/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhen Li, Xiaohan Xu, Tao Shen, et al.*2024 · 63 citations*
355. [**Exploring the Benefits of Training Expert Language Models over Instruction Tuning arXiv:2302.03202**Joel Jang, Seungone Kim, Seonghyeon Ye, et al.*2023 · 63 citations*](https://md.research.elamur.ai/free/en/papers/jang-2023-exploring-benefits-training-expert-language/)
356. [**Large Language Model Routing with Benchmark Datasets arXiv:2309.15789**Tal Shnitzer, Anthony Ou, Mirian Silva, et al.*2023 · 63 citations*](https://md.research.elamur.ai/free/en/papers/shnitzer-2023-large-language-model-routing-benchmark/)
357. [**LAB-Bench: Measuring Capabilities of Language Models for Biology Research arXiv:2407.10362**Jon M. Laurent, Joseph D. Janizek, Michael Ruzo, et al.*2024 · 62 citations*](https://md.research.elamur.ai/free/en/papers/laurent-2024-lab-bench-measuring-capabilities/)
358. [**Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use Cases arXiv:2303.14742**Yunjie Ji, Yong Deng, Yan Gong, et al.*2023 · 62 citations*](https://md.research.elamur.ai/free/en/papers/ji-2023-exploring-impact-instruction-data/)
359. [**Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves arXiv:2311.04205**Yihe Deng, Weitong Zhang, Zixiang Chen, et al.*2023 · 62 citations*](https://md.research.elamur.ai/free/en/papers/deng-2023-rephrase-respond-let-large-language/)
360. **[SatLM: Satisfiability-Aided Language Models Using Declarative Prompting](https://md.research.elamur.ai/free/en/papers/ye-2023-satlm-satisfiability-aided-language-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xi Ye, Qiaochu Chen, Isil Dillig, et al.*2023 · 62 citations*
361. [**What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization? arXiv:2204.05832**Thomas Wang, Adam Roberts, Daniel Hesslow, et al.*2022 · 62 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-language-model-architecture-pretraining-objective/)
362. **[One-Shot Learning as Instruction Data Prospector for Large Language Models](https://md.research.elamur.ai/free/en/papers/li-2024-one-shot-learning-as-instruction/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yunshui Li, Binyuan Hui, Xiaobo Xia, et al.*2024 · 61 citations*
363. [**Teaching Large Language Models to Reason with Reinforcement Learning arXiv:2403.04642**Alex Havrilla, Yuqing Du, Sharath Chandra Raparthy, et al.*2024 · 61 citations*](https://md.research.elamur.ai/free/en/papers/havrilla-2024-teaching-large-language-models-reason/)
364. [**Large Language Models Are Not Strong Abstract Reasoners arXiv:2305.19555**Gael Gendron, Qiming Bao, Michael Witbrock, et al.*2023 · 61 citations*](https://md.research.elamur.ai/free/en/papers/gendron-2023-large-language-models-strong-abstract/)
365. [**Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning arXiv:2402.00530**Ming Li, Yong Zhang, Shwai He, et al.*2024 · 60 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-superfiltering-weak-strong-data-filtering/)
366. **[Instruction Pre-Training: Language Models are Supervised Multitask Learners](https://md.research.elamur.ai/free/en/papers/cheng-2024-instruction-pre-training-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Daixuan Cheng, Yuxian Gu, Shaohan Huang, et al.*2024 · 59 citations*
367. [**Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe arXiv:2604.13016**Yaxuan Li, Yuxin Zuo, Bingxiang He, et al.*2026 · 58 citations*](https://md.research.elamur.ai/free/en/papers/li-2026-rethinking-policy-distillation-large-language/)
368. **[BIG-Bench Extra Hard](https://md.research.elamur.ai/free/en/papers/kazemi-2025-big-bench-extra-hard/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mehran Kazemi, Bahare Fatemi, Hritik Bansal, et al.*2025 · 58 citations*
369. **[SoftCoT: Soft Chain-of-Thought for Efficient Reasoning with LLMs](https://md.research.elamur.ai/free/en/papers/xu-2025-softcot-soft-chain-thought-efficient/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yige Xu, Xu Guo, Zhiwei Zeng, et al.*2025 · 58 citations*
370. **[ChatQA: Surpassing GPT-4 on Conversational QA and RAG](https://md.research.elamur.ai/free/en/papers/liu-2024-chatqa-surpassing-gpt-4-conversational/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zihan Liu, Wei Ping, Rajarshi Roy, et al.*2024 · 58 citations*
371. [**Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws arXiv:2401.00448**Nikhil Sardana, Jacob Portes, Sasha Doubov, et al.*2024 · 57 citations*](https://md.research.elamur.ai/free/en/papers/sardana-2024-chinchilla-optimal-accounting-inference-language/)
372. [**SuperCLUE: A Comprehensive Chinese Large Language Model Benchmark arXiv:2307.15020**Liang Xu, Anqi Li, Lei Zhu, et al.*2023 · 57 citations*](https://md.research.elamur.ai/free/en/papers/xu-2023-superclue-comprehensive-chinese-large/)
373. [**Teaching Arithmetic to Small Transformers arXiv:2307.03381**Nayoung Lee, Kartik Sreenivasan, Jason D. Lee, et al.*2023 · 57 citations*](https://md.research.elamur.ai/free/en/papers/lee-2023-teaching-arithmetic-small-transformers/)
374. **[The Art of Saying No: Contextual Noncompliance in Language Models](https://md.research.elamur.ai/free/en/papers/brahman-2024-art-saying-no-contextual-noncompliance/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Faeze Brahman, Sachin Kumar, Vidhisha Balachandran, et al.*2024 · 56 citations*
375. **[A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains](https://md.research.elamur.ai/free/en/papers/jacovi-2024-chain-thought-is-as-strong/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Alon Jacovi, Yonatan Bitton, Bernd Bohnet, et al.*2024 · 54 citations*
376. **[SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages](https://md.research.elamur.ai/free/en/papers/lovenia-2024-seacrowd-multilingual-multimodal-data-hub/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Holy Lovenia, Rahmad Mahendra, Salsabil Maulana Akbar, et al.*2024 · 54 citations*
377. [**CMB: A Comprehensive Medical Benchmark in Chinese arXiv:2308.08833**Xidong Wang, Guiming Hardy Chen, Dingjie Song, et al.*2023 · 54 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-cmb-comprehensive-medical-benchmark/)
378. **[Unifying AI Tutor Evaluation: An Evaluation Taxonomy for Pedagogical Ability Assessment of LLM-Powered AI Tutors](https://md.research.elamur.ai/free/en/papers/maurya-2025-unifying-ai-tutor-evaluation-evaluation/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Kaushal Kumar Maurya, Kv Aditya Srivatsa, Kseniia Petukhova, et al.*2025 · 53 citations*
379. [**Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? arXiv:2406.13121**Jinhyuk Lee, Anthony Chen, Zhuyun Dai, et al.*2024 · 53 citations*](https://md.research.elamur.ai/free/en/papers/lee-2024-long-context-language-models-subsume/)
380. [**RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and Style arXiv:2410.16184**Yantao Liu, Zijun Yao, Rui Min, et al.*2024 · 53 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-rm-bench-benchmarking-reward-models/)
381. [**Towards responsible development of generative AI for education: An evaluation-driven approach**Jurenka*2024 · 53 citations*](https://md.research.elamur.ai/free/en/papers/jurenka-2024-responsible-development-generative-ai-education/)
382. [**Language acquisition: do children and language models follow similar learning stages? arXiv:2306.03586**Linnea Evanson, Yair Lakretz, Jean-Rémi King*2023 · 53 citations*](https://md.research.elamur.ai/free/en/papers/evanson-2023-language-acquisition-children-language-models/)
383. **[The Learnability of In-Context Learning](https://md.research.elamur.ai/free/en/papers/wies-2023-learnability-context-learning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Noam Wies, Yoav Levine, Amnon Shashua*2023 · 53 citations*
384. [**Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners arXiv:2305.14825**Xiaojuan Tang, Zilong Zheng, Jiaqi Li, et al.*2023 · 52 citations*](https://md.research.elamur.ai/free/en/papers/tang-2023-large-language-models-context-semantic/)
385. **[Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision](https://md.research.elamur.ai/free/en/papers/sun-2024-easy-hard-generalization-scalable-alignment/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhiqing Sun, Longhui Yu, Yikang Shen, et al.*2024 · 51 citations*
386. [**On the application of Large Language Models for language teaching and assessment technology arXiv:2307.08393**Andrew Caines, Luca Benedetto, Shiva Taslimipoor, et al.*2023 · 51 citations*](https://md.research.elamur.ai/free/en/papers/caines-2023-application-large-language-models-language/)
387. [**How to Train Data-Efficient LLMs arXiv:2402.09668**Noveen Sachdeva, Benjamin Coleman, Wang-Cheng Kang, et al.*2024 · 50 citations*](https://md.research.elamur.ai/free/en/papers/sachdeva-2024-train-data-efficient-llms/)
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389. [**TIGERScore: Towards Building Explainable Metric for All Text Generation Tasks arXiv:2310.00752**Dongfu Jiang, Yishan Li, Ge Zhang, et al.*2023 · 50 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2023-tigerscore-towards-building-explainable-metric/)
390. **[The Effectiveness of LLMs as Annotators: A Comparative Overview and Empirical Analysis of Direct Representation](https://md.research.elamur.ai/free/en/papers/pavlovic-2024-effectiveness-llms-annotators-comparative-overview/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Maja Pavlovic, Massimo Poesio*2024 · 49 citations*
391. [**Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models arXiv:2406.02061**Marianna Nezhurina, Lucia Cipolina-Kun, Mehdi Cherti, et al.*2024 · 48 citations*](https://md.research.elamur.ai/free/en/papers/nezhurina-2024-alice-wonderland-simple-tasks-showing/)
392. **[GrammarGPT: Exploring Open-Source LLMs for Native Chinese Grammatical Error Correction with Supervised Fine-Tuning](https://md.research.elamur.ai/free/en/papers/fan-2023-grammargpt-exploring-open-source-llms/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Yaxin Fan; Feng Jiang; Peifeng Li;, Haizhou Li*2023 · 48 citations*
393. [**Linguistic ambiguity analysis in ChatGPT arXiv:2302.06426**Miguel Ortega-Martin, Oscar Garcia-Sierra, Alfonso Ardoiz, et al.*2023 · 48 citations*](https://md.research.elamur.ai/free/en/papers/ortega-martin-2023-linguistic-ambiguity-analysis-chatgpt/)
394. **[Improving the Validity of Automatically Generated Feedback via Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/scarlatos-2024-improving-validity-automatically-generated-feedback/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Alexander Scarlatos, Digory Smith, Simon Woodhead, et al.*2024 · 47 citations*
395. **[Metaphor Understanding Challenge Dataset for LLMs](https://md.research.elamur.ai/free/en/papers/tong-2024-metaphor-understanding-challenge-dataset-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaoyu Tong, Rochelle Choenni, Martha Lewis, et al.*2024 · 47 citations*
396. **[AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning](https://md.research.elamur.ai/free/en/papers/zhou-2024-autopeft-automatic-configuration-search-parameter/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Han Zhou, Xingchen Wan, Ivan Vulić, et al.*2024 · 46 citations*
397. [**Language models scale reliably with over-training and on downstream tasks arXiv:2403.08540**Samir Yitzhak Gadre, Georgios Smyrnis, Vaishaal Shankar, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/gadre-2024-language-models-scale-reliably-over/)
398. **[“According to . . . “: Prompting Language Models Improves Quoting from Pre-Training Data](https://md.research.elamur.ai/free/en/papers/weller-2024-according-prompting-language-models-improves/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Orion Weller, Marc Marone, Nathaniel Weir, et al.*2024 · 46 citations*
399. [**GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements arXiv:2402.10963**Alex Havrilla, Sharath Raparthy, Christoforus Nalmpantis, et al.*2024 · 45 citations*](https://md.research.elamur.ai/free/en/papers/havrilla-2024-glore-where-improve-llm-reasoning/)
400. [**Lost in Translation: Large Language Models in Non-English Content Analysis arXiv:2306.07377**Gabriel Nicholas, Aliya Bhatia*2023 · 45 citations*](https://md.research.elamur.ai/free/en/papers/nicholas-2023-lost-translation-large-language-models/)

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