---
title: "Who cites Christopher E. Potts — page 10 — 9,352 citing papers"
description: "9,352 papers of the Elamur Research library cite the works of Christopher E. Among them: KoBBQ: Korean Bias Benchmark for Question…"
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---
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# Who cites Christopher E. Potts

11,943 citations of their works from 9,352 papers of our library (of the 149,439 whose bibliographies we read) · 1,871 with a page here, the most cited first · page 10 of 19 · 901–1000 · [← Christopher E. Potts](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/)

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901. **[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*
902. [**Are Multilingual LLMs Culturally-Diverse Reasoners? An Investigation into Multicultural Proverbs and Sayings arXiv:2309.08591**Chen Cecilia Liu, Fajri Koto, Timothy Baldwin, et al.*2023 · 78 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-multilingual-llms-culturally-diverse-reasoners/)
903. **[Finding Support Examples for In-Context Learning](https://md.research.elamur.ai/free/en/papers/li-2023-finding-support-examples-context-learning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiaonan Li, Xipeng Qiu*2023 · 78 citations*
904. **[Language models are not naysayers: An analysis of language models on negation benchmarks](https://md.research.elamur.ai/free/en/papers/truong-2023-language-models-naysayers-analysis-language/) [Joint Conference on Lexical and Computational Semantics (*SEM)](https://md.research.elamur.ai/free/en/venues/starsem/)**Thinh Hung Truong, Timothy Baldwin, Karin Verspoor, et al.*2023 · 78 citations*
905. [**Pragmatically Informative Text Generation**Sheng Shen, Daniel Fried, Jacob Andreas, et al.*2019 · 78 citations*](https://md.research.elamur.ai/free/en/papers/shen-2019-pragmatically-informative-text-generation/)
906. [**Learning in the Rational Speech Acts Model arXiv:1510.06807**Will Monroe, Christopher Potts*2015 · 78 citations*](https://md.research.elamur.ai/free/en/papers/monroe-2015-learning-rational-speech-acts-model/)
907. **[Contrast and the structure of discourse](https://md.research.elamur.ai/free/en/papers/toosarvandani-2014-contrast-structure-discourse/) [Semantics and Pragmatics](https://md.research.elamur.ai/free/en/venues/semantics-and-pragmatics/)**Maziar Toosarvandani*2014 · 78 citations*
908. **[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*
909. **[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*
910. **[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*
911. [**Localizing Model Behavior with Path Patching arXiv:2304.05969**Nicholas Goldowsky-Dill, Chris MacLeod, Lucas Sato, et al.*2023 · 76 citations*](https://md.research.elamur.ai/free/en/papers/goldowsky-dill-2023-localizing-model-behavior-path-patching/)
912. [**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/)
913. [**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/)
914. **[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*
915. [**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/)
916. [**Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents arXiv:2310.19923**Michael Gunther, Jackmin Ong, Isabelle Mohr, et al.*2023 · 75 citations*](https://md.research.elamur.ai/free/en/papers/gunther-2023-jina-embeddings-2-8192-token/)
917. **[A Joint Framework for Coreference Resolution and Mention Head Detection](https://md.research.elamur.ai/free/en/papers/peng-2015-joint-framework-coreference-resolution-mention/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Haoruo Peng, Kai-Wei Chang, Dan Roth*2015 · 75 citations*
918. **[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*
919. [**Gecko: Versatile Text Embeddings Distilled from Large Language Models arXiv:2403.20327**Jinhyuk Lee, Zhuyun Dai, Xiaoqi Ren, et al.*2024 · 74 citations*](https://md.research.elamur.ai/free/en/papers/lee-2024-gecko-versatile-text-embeddings-distilled/)
920. [**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/)
921. **[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*
922. **[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*
923. **[ReFT: Representation Finetuning for Language Models](https://md.research.elamur.ai/free/en/papers/wu-2024-reft-representation-finetuning-language-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhengxuan Wu, Aryaman Arora, Zheng Wang, et al.*2024 · 74 citations*
924. [**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/)
925. [**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/)
926. [**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/)
927. [**Annotation Error Detection: Analyzing the Past and Present for a More Coherent Future**Jan-Christoph Klie, Bonnie Webber, Iryna Gurevych*2023 · 73 citations*](https://md.research.elamur.ai/free/en/papers/klie-2023-annotation-error-detection-analyzing-past/)
928. **[ReCEval: Evaluating Reasoning Chains via Correctness and Informativeness](https://md.research.elamur.ai/free/en/papers/prasad-2023-receval-evaluating-reasoning-chains-correctness/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Archiki Prasad, Swarnadeep Saha, Xiang Zhou, et al.*2023 · 73 citations*
929. [**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/)
930. [**The discourse commitments of illocutionary reportatives**Martina T Faller*2019 · 73 citations*](https://md.research.elamur.ai/free/en/papers/faller-2019-discourse-commitments-illocutionary-reportatives/)
931. [**A Primer on the Inner Workings of Transformer-based Language Models arXiv:2405.00208**Javier Ferrando, Gabriele Sarti, Arianna Bisazza, et al.*2024 · 72 citations*](https://md.research.elamur.ai/free/en/papers/ferrando-2024-primer-inner-workings-transformer-based/)
932. [**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/)
933. [**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/)
934. **[Transformer Grammars: Augmenting Transformer Language Models with Syntactic Inductive Biases at Scale](https://md.research.elamur.ai/free/en/papers/sartran-2022-transformer-grammars-augmenting-transformer-language/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Laurent Sartran, Samuel Barrett, Adhiguna Kuncoro, et al.*2022 · 72 citations*
935. [**"I'd rather just go to bed": Understanding Indirect Answers**Annie Louis, Dan Roth, Filip Radlinski*2020 · 72 citations*](https://md.research.elamur.ai/free/en/papers/louis-2020-i-d-rather-just-go/)
936. **[Finding the Optimal Vocabulary Size for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/gowda-2020-finding-optimal-vocabulary-size-neural/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Thamme Gowda, Jonathan May*2020 · 72 citations*
937. **[Evaluating BERT for natural language inference: A case study on the CommitmentBank](https://md.research.elamur.ai/free/en/papers/jiang-2019-evaluating-bert-natural-language-inference/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Nanjiang Jiang, Marie-Catherine de Marneffe*2019 · 72 citations*
938. **[IrokoBench: A New Benchmark for African Languages in the Age of Large Language Models](https://md.research.elamur.ai/free/en/papers/adelani-2025-irokobench-new-benchmark-african-languages/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**David Ifeoluwa Adelani, Jessica Ojo, Israel Abebe Azime, et al.*2025 · 70 citations*
939. **[Mission: Impossible Language Models](https://md.research.elamur.ai/free/en/papers/kallini-2024-mission-impossible-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Julie Kallini, Isabel Papadimitriou, Richard Futrell, et al.*2024 · 70 citations*
940. **[Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback](https://md.research.elamur.ai/free/en/papers/roit-2023-factually-consistent-summarization-reinforcement-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Paul Roit, Johan Ferret, Lior Shani, et al.*2023 · 70 citations*
941. **[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*
942. [**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/)
943. **[Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training](https://md.research.elamur.ai/free/en/papers/stacey-2020-avoiding-hypothesis-only-bias-natural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Joe Stacey, Pasquale Minervini, Haim Dubossarsky, et al.*2020 · 70 citations*
944. **[English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too](https://md.research.elamur.ai/free/en/papers/phang-2020-english-intermediate-task-training-improves/) [Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL)](https://md.research.elamur.ai/free/en/venues/aacl/)**Jason Phang, Iacer Calixto, Phu Mon Htut, et al.*2020 · 70 citations*
945. **[Towards Robustifying NLI Models Against Lexical Dataset Biases](https://md.research.elamur.ai/free/en/papers/zhou-2020-robustifying-nli-models-against-lexical/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiang Zhou, Mohit Bansal*2020 · 70 citations*
946. **[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*
947. [**The Turking Test: Can Language Models Understand Instructions? arXiv:2010.11982**Avia Efrat, Omer Levy*2020 · 69 citations*](https://md.research.elamur.ai/free/en/papers/efrat-2020-turking-test-language-models-understand/)
948. [**Graph based Neural Networks for Event Factuality Prediction using Syntactic and Semantic Structures**Amir Pouran Ben Veyseh, Thien Huu Nguyen, Dejing Dou*2019 · 69 citations*](https://md.research.elamur.ai/free/en/papers/veyseh-2019-graph-based-neural-networks-event/)
949. **[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*
950. [**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/)
951. [**IMPLI: Investigating NLI Models' Performance on Figurative Language**Kevin Stowe, Prasetya Utama, Iryna Gurevych*2022 · 68 citations*](https://md.research.elamur.ai/free/en/papers/stowe-2022-impli-investigating-nli-models-performance/)
952. **[A Natural Language Corpus of Common Grounding under Continuous and Partially-Observable Context](https://md.research.elamur.ai/free/en/papers/udagawa-2019-natural-language-corpus-common-grounding/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Takuma Udagawa, Akiko Aizawa*2019 · 68 citations*
953. **[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*
954. **[Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue](https://md.research.elamur.ai/free/en/papers/gu-2024-model-editing-harms-general-abilities/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jia-Chen Gu, Hao-Xiang Xu, Jun-Yu Ma, et al.*2024 · 67 citations*
955. [**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/)
956. [**VALUE: Understanding Dialect Disparity in NLU**Caleb Ziems, Jiaao Chen, Camille Harris, et al.*2022 · 67 citations*](https://md.research.elamur.ai/free/en/papers/ziems-2022-value-understanding-dialect-disparity-nlu/)
957. **[A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models](https://md.research.elamur.ai/free/en/papers/baly-2017-characterization-study-arabic-twitter-data/) [Arabic Natural Language Processing Workshop (WANLP)](https://md.research.elamur.ai/free/en/venues/wanlp/)**Ramy Baly, Gilbert Badaro, Georges El-Khoury, et al.*2017 · 67 citations*
958. **[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*
959. [**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/)
960. **[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*
961. **[ClashEval: Quantifying the tug-of-war between an LLM's internal prior and external evidence](https://md.research.elamur.ai/free/en/papers/wu-2024-clasheval-quantifying-tug-war-between/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Kevin Wu, Eric Wu, James Zou*2024 · 65 citations*
962. **[Generate-then-Ground in Retrieval-Augmented Generation for Multi-hop Question Answering](https://md.research.elamur.ai/free/en/papers/shi-2024-generate-then-ground-retrieval-augmented/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhengliang Shi, Shuo Zhang, Weiwei Sun, et al.*2024 · 65 citations*
963. [**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/)
964. [**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/)
965. **[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*
966. **[ParsiNLU: A Suite of Language Understanding Challenges for Persian](https://md.research.elamur.ai/free/en/papers/khashabi-2021-parsinlu-suite-language-understanding-challenges/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Daniel Khashabi, Arman Cohan, Siamak Shakeri, et al.*2021 · 65 citations*
967. **[Idiom Token Classification using Sentential Distributed Semantics](https://md.research.elamur.ai/free/en/papers/salton-2016-idiom-token-classification-sentential-distributed/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Giancarlo Salton, Robert Ross, John Kelleher*2016 · 65 citations*
968. [**Layer by Layer: Uncovering Hidden Representations in Language Models arXiv:2502.02013**Oscar Skean, Md Rifat Arefin, Dan Zhao, et al.*2025 · 64 citations*](https://md.research.elamur.ai/free/en/papers/skean-2025-layer-layer-uncovering-hidden-representations/)
969. **[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*
970. [**Evaluation of OpenAI o1: Opportunities and Challenges of AGI arXiv:2409.18486**Tianyang Zhong, Zhengliang Liu, Yi Pan, et al.*2024 · 64 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2024-evaluation-openai-o1-opportunities-challenges/)
971. [**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/)
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973. **[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*
974. **[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*
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