---
title: "Who cites Dorottya Demszky — page 2 — 1,595 citing papers"
description: "1,595 papers of the Elamur Research library cite the works of Dorottya Demszky, found in their bibliographies · page 2 of 4 · 101–200; the…"
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---
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# Who cites Dorottya Demszky

1,664 citations of their works from 1,595 papers of our library (of the 149,439 whose bibliographies we read) · 303 with a page here, the most cited first · page 2 of 4 · 101–200 · [← Dorottya Demszky](https://md.research.elamur.ai/free/en/authors/dorottya-demszky/)

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101. [**Language models show human-like content effects on reasoning tasks arXiv:2207.07051**Ishita Dasgupta, Andrew K. Lampinen, Stephanie C. Y. Chan, et al.*2022 · 162 citations*](https://md.research.elamur.ai/free/en/papers/dasgupta-2022-language-models-show-human-like/)
102. [**DART: Open-Domain Structured Data Record to Text Generation arXiv:2007.02871**Linyong Nan, Dragomir Radev, Rui Zhang, et al.*2020 · 161 citations*](https://md.research.elamur.ai/free/en/papers/nan-2020-dart-open-domain-structured-data/)
103. [**Leveraging Large Language Models for Multiple Choice Question Answering arXiv:2210.12353**Joshua Robinson, Christopher Michael Rytting, David Wingate*2022 · 157 citations*](https://md.research.elamur.ai/free/en/papers/robinson-2022-leveraging-large-language-models-multiple/)
104. [**Ask Me Anything: A simple strategy for prompting language models arXiv:2210.02441**Simran Arora, Avanika Narayan, Mayee F. Chen, et al.*2022 · 154 citations*](https://md.research.elamur.ai/free/en/papers/arora-2022-ask-me-anything-simple-strategy/)
105. **[ProsocialDialog: A Prosocial Backbone for Conversational Agents](https://md.research.elamur.ai/free/en/papers/kim-2022-prosocialdialog-prosocial-backbone-conversational-agents/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Hyunwoo Kim, Youngjae Yu, Liwei Jiang, et al.*2022 · 152 citations*
106. **[Towards Question-Answering as an Automatic Metric for Evaluating the Content Quality of a Summary](https://md.research.elamur.ai/free/en/papers/deutsch-2021-question-answering-as-automatic-metric/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Daniel Deutsch, Tania Bedrax-Weiss, Dan Roth*2021 · 152 citations*
107. [**Unsolved Problems in ML Safety arXiv:2109.13916**Dan Hendrycks, Nicholas Carlini, John Schulman, et al.*2021 · 151 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2021-unsolved-problems-ml-safety/)
108. [**MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge arXiv:2206.08853**Linxi Fan, Guanzhi Wang, Yunfan Jiang, et al.*2022 · 149 citations*](https://md.research.elamur.ai/free/en/papers/fan-2022-minedojo-building-open-ended-embodied/)
109. **[Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding](https://md.research.elamur.ai/free/en/papers/zhang-2024-draft-verify-lossless-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jun Zhang, Jue Wang, Huan Li, et al.*2024 · 148 citations*
110. [**MiniLLM: On-Policy Distillation of Large Language Models arXiv:2306.08543**Yuxian Gu, Li Dong, Furu Wei, et al.*2023 · 144 citations*](https://md.research.elamur.ai/free/en/papers/gu-2023-minillm-policy-distillation-large-language/)
111. [**InternVideo: General Video Foundation Models via Generative and Discriminative Learning arXiv:2212.03191**Yi Wang, Kunchang Li, Yizhuo Li, et al.*2022 · 144 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-internvideo-general-video-foundation/)
112. [**A Roadmap to Pluralistic Alignment arXiv:2402.05070**Taylor Sorensen, Jared Moore, Jillian Fisher, et al.*2024 · 143 citations*](https://md.research.elamur.ai/free/en/papers/sorensen-2024-roadmap-pluralistic-alignment/)
113. [**DeepInception: Hypnotize Large Language Model to Be Jailbreaker arXiv:2311.03191**Xuan Li, Zhanke Zhou, Jianing Zhu, et al.*2023 · 141 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-deepinception-hypnotize-large-language-model/)
114. [**Long-context LLMs Struggle with Long In-context Learning arXiv:2404.02060**Tianle Li, Ge Zhang, Quy Duc Do, et al.*2024 · 138 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-long-context-llms-struggle-long/)
115. [**LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks arXiv:2412.15204**Yushi Bai, Shangqing Tu, Jiajie Zhang, et al.*2024 · 135 citations*](https://md.research.elamur.ai/free/en/papers/bai-2024-longbench-v2-deeper-understanding/)
116. [**Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions arXiv:2309.07875**Federico Bianchi, Mirac Suzgun, Giuseppe Attanasio, et al.*2023 · 135 citations*](https://md.research.elamur.ai/free/en/papers/bianchi-2023-safety-tuned-llamas-lessons-improving/)
117. [**Secrets of RLHF in Large Language Models Part I: PPO arXiv:2307.04964**Rui Zheng, Shihan Dou, Songyang Gao, et al.*2023 · 135 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2023-secrets-rlhf-large-language-models/)
118. **[Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View](https://md.research.elamur.ai/free/en/papers/zhang-2024-exploring-collaboration-mechanisms-llm-agents/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jintian Zhang, Xin Xu, Ningyu Zhang, et al.*2024 · 134 citations*
119. **[Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset](https://md.research.elamur.ai/free/en/papers/henderson-2022-pile-law-learning-responsible-data/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Peter Henderson, Mark S. Krass, Lucia Zheng, et al.*2022 · 133 citations*
120. **[MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning Problems](https://md.research.elamur.ai/free/en/papers/macina-2023-mathdial-dialogue-tutoring-dataset-rich/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jakub Macina, Nico Daheim, Sankalan Chowdhury, et al.*2023 · 131 citations*
121. **[InfLLM: Training-Free Long-Context Extrapolation for LLMs with an Efficient Context Memory](https://md.research.elamur.ai/free/en/papers/xiao-2024-infllm-training-free-long-context/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Chaojun Xiao, Pengle Zhang, Xu Han, et al.*2024 · 126 citations*
122. **[ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts](https://md.research.elamur.ai/free/en/papers/asai-2022-attempt-parameter-efficient-multi-task/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Akari Asai, Mohammadreza Salehi, Matthew Peters, et al.*2022 · 122 citations*
123. [**The Alignment Problem from a Deep Learning Perspective arXiv:2209.00626**Richard Ngo, Lawrence Chan, Soren Mindermann*2022 · 121 citations*](https://md.research.elamur.ai/free/en/papers/ngo-2022-alignment-problem-deep-learning-perspective/)
124. [**Med-Flamingo: a Multimodal Medical Few-shot Learner arXiv:2307.15189**Michael Moor, Qian Huang, Shirley Wu, et al.*2023 · 116 citations*](https://md.research.elamur.ai/free/en/papers/moor-2023-med-flamingo-multimodal-medical-few/)
125. [**ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning arXiv:2111.10952**Vamsi Aribandi, Yi Tay, Tal Schuster, et al.*2021 · 114 citations*](https://md.research.elamur.ai/free/en/papers/aribandi-2021-ext5-towards-extreme-multi-task/)
126. [**PaperQA: Retrieval-Augmented Generative Agent for Scientific Research arXiv:2312.07559**Jakub Lála, Odhran O'Donoghue, Aleksandar Shtedritski, et al.*2023 · 109 citations*](https://md.research.elamur.ai/free/en/papers/lala-2023-paperqa-retrieval-augmented-generative/)
127. **[Benchmarking Foundation Models with Language-Model-as-an-Examiner](https://md.research.elamur.ai/free/en/papers/bai-2023-benchmarking-foundation-models-language-model/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yushi Bai, Jiahao Ying, Yixin Cao, et al.*2023 · 106 citations*
128. [**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 · 105 citations*](https://md.research.elamur.ai/free/en/papers/mckenzie-2023-inverse-scaling-bigger-isn-t/)
129. [**Studying Large Language Model Generalization with Influence Functions arXiv:2308.03296**Roger Grosse, Juhan Bae, Cem Anil, et al.*2023 · 105 citations*](https://md.research.elamur.ai/free/en/papers/grosse-2023-studying-large-language-model-generalization/)
130. [**Shadow Alignment: The Ease of Subverting Safely-Aligned Language Models arXiv:2310.02949**Xianjun Yang, Xiao Wang, Qi Zhang, et al.*2023 · 103 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-shadow-alignment-ease-subverting-safely/)
131. [**Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing arXiv:2404.14618**Dujian Ding, Ankur Mallick, Chi Wang, et al.*2024 · 98 citations*](https://md.research.elamur.ai/free/en/papers/ding-2024-hybrid-llm-cost-efficient-quality/)
132. [**TextGrad: Automatic "Differentiation" via Text arXiv:2406.07496**Mert Yuksekgonul, Federico Bianchi, Joseph Boen, et al.*2024 · 96 citations*](https://md.research.elamur.ai/free/en/papers/yuksekgonul-2024-textgrad-automatic-differentiation-text/)
133. [**The Platonic Representation Hypothesis arXiv:2405.07987**Minyoung Huh, Brian Cheung, Tongzhou Wang, et al.*2024 · 96 citations*](https://md.research.elamur.ai/free/en/papers/huh-2024-platonic-representation-hypothesis/)
134. **[Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes](https://md.research.elamur.ai/free/en/papers/arora-2023-language-models-enable-simple-systems/) [Proceedings of the VLDB Endowment](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-vldb-endowment/)**Simran Arora, Brandon Yang, Sabri Eyuboglu, et al.*2023 · 94 citations*
135. [**Training Socially Aligned Language Models on Simulated Social Interactions arXiv:2305.16960**Ruibo Liu, Ruixin Yang, Chenyan Jia, et al.*2023 · 94 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-training-socially-aligned-language-models/)
136. [**Language Modeling Is Compression arXiv:2309.10668**Gregoire Deletang, Anian Ruoss, Paul-Ambroise Duquenne, et al.*2023 · 91 citations*](https://md.research.elamur.ai/free/en/papers/deletang-2023-language-modeling-compression/)
137. **[Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards](https://md.research.elamur.ai/free/en/papers/rame-2023-rewarded-soups-towards-pareto-optimal/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Alexandre Rame, Guillaume Couairon, Mustafa Shukor, et al.*2023 · 90 citations*
138. [**Automatically Auditing Large Language Models via Discrete Optimization arXiv:2303.04381**Erik Jones, Anca Dragan, Aditi Raghunathan, et al.*2023 · 89 citations*](https://md.research.elamur.ai/free/en/papers/jones-2023-automatically-auditing-large-language-models/)
139. **[Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Prediction](https://md.research.elamur.ai/free/en/papers/troiano-2023-dimensional-modeling-emotions-text-appraisal/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Enrica Troiano, Laura Oberländer, Roman Klinger*2023 · 89 citations*
140. **[EmoBench: Evaluating the Emotional Intelligence of Large Language Models](https://md.research.elamur.ai/free/en/papers/sabour-2024-emobench-evaluating-emotional-intelligence-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sahand Sabour, Siyang Liu, Zheyuan Zhang, et al.*2024 · 88 citations*
141. [**Large Language Models as General Pattern Machines arXiv:2307.04721**Suvir Mirchandani, Fei Xia, Pete Florence, et al.*2023 · 88 citations*](https://md.research.elamur.ai/free/en/papers/mirchandani-2023-large-language-models-general-pattern/)
142. [**The AI teacher test: Measuring the pedagogical ability of Blender and GPT-3 in educational dialogues**Tack*2022 · 88 citations*](https://md.research.elamur.ai/free/en/papers/tack-2022-ai-teacher-test-measuring-pedagogical/)
143. **[Complex Claim Verification with Evidence Retrieved in the Wild](https://md.research.elamur.ai/free/en/papers/chen-2024-complex-claim-verification-evidence-retrieved/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Jifan Chen, Grace Kim, Aniruddh Sriram, et al.*2024 · 86 citations*
144. [**Mathematics, word problems, common sense, and artificial intelligence arXiv:2301.09723**Ernest Davis*2023 · 86 citations*](https://md.research.elamur.ai/free/en/papers/davis-2023-mathematics-word-problems-common-sense/)
145. **[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*
146. **[Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation](https://md.research.elamur.ai/free/en/papers/fernandes-2023-bridging-gap-survey-integrating-human/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Patrick Fernandes, Aman Madaan, Emmy Liu, et al.*2023 · 84 citations*
147. [**ArtPrompt: ASCII Art-based Jailbreak Attacks against Aligned LLMs arXiv:2402.11753**Fengqing Jiang, Zhangchen Xu, Luyao Niu, et al.*2024 · 83 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2024-artprompt-ascii-art-based-jailbreak/)
148. [**Does Writing with Language Models Reduce Content Diversity? arXiv:2309.05196**Vishakh Padmakumar, He He*2023 · 82 citations*](https://md.research.elamur.ai/free/en/papers/padmakumar-2023-writing-language-models-reduce-content/)
149. [**Large Language Models in Law: A Survey arXiv:2312.03718**Jinqi Lai, Wensheng Gan, Jiayang Wu, et al.*2023 · 80 citations*](https://md.research.elamur.ai/free/en/papers/lai-2023-large-language-models-law-survey/)
150. [**LEGO-Prover: Neural Theorem Proving with Growing Libraries arXiv:2310.00656**Haiming Wang, Huajian Xin, Chuanyang Zheng, et al.*2023 · 79 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-lego-prover-neural-theorem-proving/)
151. **[AudioBench: A Universal Benchmark for Audio Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2025-audiobench-universal-benchmark-audio-large/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Bin Wang, Xunlong Zou, Geyu Lin, et al.*2025 · 77 citations*
152. **[Analyzing Dataset Annotation Quality Management in the Wild](https://md.research.elamur.ai/free/en/papers/klie-2024-analyzing-dataset-annotation-quality-management/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Jan-Christoph Klie, Richard Eckart de Castilho, Iryna Gurevych*2024 · 76 citations*
153. **[Measuring Political Bias in Large Language Models: What Is Said and How It Is Said](https://md.research.elamur.ai/free/en/papers/bang-2024-measuring-political-bias-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yejin Bang, Delong Chen, Nayeon Lee, et al.*2024 · 76 citations*
154. [**Multi-VALUE: A Framework for Cross-Dialectal English NLP**Caleb Ziems, William Held, Jingfeng Yang, et al.*2023 · 76 citations*](https://md.research.elamur.ai/free/en/papers/ziems-2023-multi-value-framework-cross-dialectal/)
155. [**The Ghost in the Machine has an American accent: value conflict in GPT-3 arXiv:2203.07785**Rebecca L Johnson, Giada Pistilli, Natalia Menedez-Gonzalez, et al.*2022 · 72 citations*](https://md.research.elamur.ai/free/en/papers/johnson-2022-ghost-machine-has-american-accent/)
156. **[Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning](https://md.research.elamur.ai/free/en/papers/tafjord-2022-entailer-answering-questions-faithful/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Oyvind Tafjord, Bhavana Dalvi Mishra, Peter Clark*2022 · 71 citations*
157. [**LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations arXiv:2410.02707**Hadas Orgad, Michael Toker, Zorik Gekhman, et al.*2024 · 68 citations*](https://md.research.elamur.ai/free/en/papers/orgad-2024-llms-know-more-than-they/)
158. **[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*
159. **[Can Language Models Handle Recursively Nested Grammatical Structures? A Case Study on Comparing Models and Humans](https://md.research.elamur.ai/free/en/papers/lampinen-2024-can-language-models-handle-recursively/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Andrew Lampinen*2024 · 63 citations*
160. [**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/)
161. [**Large Language Model Agent: A Survey on Methodology, Applications and Challenges arXiv:2503.21460**Junyu Luo, Weizhi Zhang, Ye Yuan, et al.*2025 · 62 citations*](https://md.research.elamur.ai/free/en/papers/luo-2025-large-language-model-agent-survey/)
162. [**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/)
163. **[NaturalProver: Grounded Mathematical Proof Generation with Language Models](https://md.research.elamur.ai/free/en/papers/welleck-2022-naturalprover-grounded-mathematical-proof-generation/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Sean Welleck, Jiacheng Liu, Ximing Lu, et al.*2022 · 62 citations*
164. **[POLAR: A Benchmark for Multilingual, Multicultural, and Multi-Event Online Polarization](https://md.research.elamur.ai/free/en/papers/naseem-2026-polar-benchmark-multilingual-multicultural-multi/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Usman Naseem, Robert Geislinger, Juan Ren, et al.*2026 · 61 citations*
165. [**OS-Copilot: Towards Generalist Computer Agents with Self-Improvement arXiv:2402.07456**Zhiyong Wu, Chengcheng Han, Zichen Ding, et al.*2024 · 61 citations*](https://md.research.elamur.ai/free/en/papers/wu-2024-os-copilot-towards-generalist-computer/)
166. [**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/)
167. **[A Family of Pretrained Transformer Language Models for Russian](https://md.research.elamur.ai/free/en/papers/zmitrovich-2024-family-pretrained-transformer-language-models/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Dmitry Zmitrovich, Aleksandr Abramov, Andrey Kalmykov, et al.*2024 · 60 citations*
168. [**Simple linear attention language models balance the recall-throughput tradeoff arXiv:2402.18668**Simran Arora, Sabri Eyuboglu, Michael Zhang, et al.*2024 · 59 citations*](https://md.research.elamur.ai/free/en/papers/arora-2024-simple-linear-attention-language-models/)
169. [**Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback arXiv:2303.05453**Hannah Rose Kirk, Bertie Vidgen, Paul Rottger, et al.*2023 · 57 citations*](https://md.research.elamur.ai/free/en/papers/kirk-2023-personalisation-within-bounds-risk-taxonomy/)
170. [**A Reasoning-Focused Legal Retrieval Benchmark arXiv:2505.03970**Lucia Zheng, Neel Guha, Javokhir Arifov, et al.*2025 · 55 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2025-reasoning-focused-legal-retrieval-benchmark/)
171. **[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*
172. [**Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models — A Survey arXiv:2404.01869**Philipp Mondorf, Barbara Plank*2024 · 53 citations*](https://md.research.elamur.ai/free/en/papers/mondorf-2024-accuracy-evaluating-reasoning-behavior-large/)
173. **[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*
174. **[Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding](https://md.research.elamur.ai/free/en/papers/belay-2025-evaluating-capabilities-large-language-models/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Tadesse Destaw Belay, Israel Abebe Azime, Abinew Ali Ayele, et al.*2025 · 52 citations*
175. [**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/)
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177. **[Can Multiple-choice Questions Really Be Useful in Detecting the Abilities of LLMs?](https://md.research.elamur.ai/free/en/papers/li-2024-can-multiple-choice-questions-really/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Wangyue Li, Liangzhi Li, Tong Xiang, et al.*2024 · 47 citations*
178. [**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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180. [**Halo: Estimation and Reduction of Hallucinations in Open-Source Weak Large Language Models arXiv:2308.11764**Mohamed Elaraby, Mengyin Lu, Jacob Dunn, et al.*2023 · 43 citations*](https://md.research.elamur.ai/free/en/papers/elaraby-2023-halo-estimation-reduction-hallucinations-open/)
181. [**SimpleSafetyTests: a Test Suite for Identifying Critical Safety Risks in Large Language Models arXiv:2311.08370**Bertie Vidgen, Nino Scherrer, Hannah Rose Kirk, et al.*2023 · 42 citations*](https://md.research.elamur.ai/free/en/papers/vidgen-2023-simplesafetytests-test-suite-identifying-critical/)
182. [**When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Tasks arXiv:2311.08993**Hao Peng, Xiaozhi Wang, Jianhui Chen, et al.*2023 · 42 citations*](https://md.research.elamur.ai/free/en/papers/peng-2023-context-learning-fall-short-why/)
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184. [**Introducing v0.5 of the AI Safety Benchmark from MLCommons arXiv:2404.12241**Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed, et al.*2024 · 38 citations*](https://md.research.elamur.ai/free/en/papers/vidgen-2024-introducing-v0-5-ai-safety/)
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186. **[JASMINE: Arabic GPT Models for Few-Shot Learning](https://md.research.elamur.ai/free/en/papers/nagoudi-2023-jasmine-arabic-gpt-models-few/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**El Moatez Billah Nagoudi, Muhammad Abdul-Mageed, AbdelRahim Elmadany, et al.*2023 · 38 citations*
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189. [**Is ChatGPT a Good Teacher Coach? Measuring Zero-Shot Performance For Scoring and Providing Actionable Insights on Classroom Instruction arXiv:2306.03090**Rose E. Wang, Dorottya Demszky*2023 · 36 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-chatgpt-good-teacher-coach-measuring/)
190. [**Linear algebra with transformers arXiv:2112.01898**François Charton*2021 · 31 citations*](https://md.research.elamur.ai/free/en/papers/charton-2021-linear-algebra-transformers/)
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192. [**Taqyim: Evaluating Arabic NLP Tasks Using ChatGPT Models arXiv:2306.16322**Zaid Alyafeai, Maged S. Alshaibani, Badr AlKhamissi, et al.*2023 · 25 citations*](https://md.research.elamur.ai/free/en/papers/alyafeai-2023-taqyim-evaluating-arabic-nlp-tasks/)
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194. [**Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models arXiv:2005.14709**Viktor Schlegel, Goran Nenadic, Riza Batista-Navarro*2020 · 24 citations*](https://md.research.elamur.ai/free/en/papers/schlegel-2020-leaderboards-survey-methods-revealing-weaknesses/)
195. [**Language Model Alignment in Multilingual Trolley Problems arXiv:2407.02273**Zhijing Jin, Max Kleiman-Weiner, Giorgio Piatti, et al.*2024 · 23 citations*](https://md.research.elamur.ai/free/en/papers/jin-2024-language-model-alignment-multilingual-trolley/)
196. [**MiniCPM4: Ultra-Efficient LLMs on End Devices arXiv:2506.07900**MiniCPM Team, Chaojun Xiao, Yuxuan Li, et al.*2025 · 22 citations*](https://md.research.elamur.ai/free/en/papers/minicpm-team-2025-minicpm4-ultra-efficient-llms/)
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198. **[AraMUS: Pushing the Limits of Data and Model Scale for Arabic Natural Language Processing](https://md.research.elamur.ai/free/en/papers/alghamdi-2023-aramus-pushing-limits-data-model/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Asaad Alghamdi, Xinyu Duan, Wei Jiang, et al.*2023 · 21 citations*
199. [**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/)
200. [**The Vector Grounding Problem arXiv:2304.01481**Dimitri Coelho Mollo, Raphael Milliere*2023 · 20 citations*](https://md.research.elamur.ai/free/en/papers/mollo-2023-vector-grounding-problem/)

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