🔗 Who cites «Language models show human-like content effects on reasoning tasks»

112 papers of our library cite it (of the 149,439 whose bibliographies we read) · 37 with a page here, the most cited first · ← back to «Language models show human-like content effects on reasoning tasks»

1. The Rise and Potential of Large Language Model Based Agents: A SurveyScience China Information SciencesZhiheng Xi, Wenxiang Chen, Xin Guo, et al.2023 · 1,272 citations
2. Towards Reasoning in Large Language Models: A SurveyFindings of the Association for Computational LinguisticsJie Huang, Kevin Chen-Chuan Chang2023 · 780 citations
3. Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought PromptingConference on Neural Information Processing Systems (NeurIPS)Miles Turpin, Julian Michael, Ethan Perez, et al.2023 · 586 citations
4. Augmented Language Models: a SurveyarXiv:2302.07842Grégoire Mialon, Roberto Dessì, Maria Lomeli, et al.2023 · 369 citations
5. Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-ThoughtarXiv:2210.01240Abulhair Saparov, He He2022 · 244 citations
6. FOLIO: Natural Language Reasoning with First-Order LogicarXiv:2209.00840Simeng Han, Hailey Schoelkopf, Yilun Zhao, et al.2022 · 219 citations
7. Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Mosh Levy, Alon Jacoby, Yoav Goldberg2024 · 217 citations
8. Sources of Hallucination by Large Language Models on Inference TasksFindings of the Association for Computational LinguisticsNick McKenna, Tianyi Li, Liang Cheng, et al.2023 · 212 citations
9. PersonaLLM: Investigating the Ability of Large Language Models to Express Personality TraitsFindings of the Association for Computational LinguisticsHang Jiang, Xiajie Zhang, Xubo Cao, et al.2023 · 211 citations
10. Evaluating and Inducing Personality in Pre-trained Language ModelsConference on Neural Information Processing Systems (NeurIPS)Guangyuan Jiang, Manjie Xu, Song-Chun Zhu, et al.2022 · 168 citations
11. Do LLMs Exhibit Human-like Response Biases? A Case Study in Survey DesignTransactions of the Association for Computational Linguistics (TACL)Lindia Tjuatja, Valerie Chen, Tongshuang Wu, et al.2024 · 140 citations
12. MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning ProblemsFindings of the Association for Computational LinguisticsJakub Macina, Nico Daheim, Sankalan Chowdhury, et al.2023 · 131 citations
13. Natural Language Reasoning, A SurveyACM Computing SurveysFei Yu, Hongbo Zhang, Prayag Tiwari, et al.2023 · 130 citations
14. Faithful Reasoning Using Large Language ModelsarXiv:2208.14271Antonia Creswell, Murray Shanahan2022 · 122 citations
15. A Peek into Token Bias: Large Language Models Are Not Yet Genuine ReasonersConference on Empirical Methods in Natural Language Processing (EMNLP)Bowen Jiang, Yangxinyu Xie, Zhuoqun Hao, et al.2024 · 64 citations
16. Can Language Models Handle Recursively Nested Grammatical Structures? A Case Study on Comparing Models and HumansComputational LinguisticsAndrew Lampinen2024 · 63 citations
17. Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models — A SurveyarXiv:2404.01869Philipp Mondorf, Barbara Plank2024 · 53 citations
18. Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language ModelsarXiv:2310.00836Man Luo, Shrinidhi Kumbhar, Ming shen, et al.2023 · 27 citations
19. SemEval-2026 Task 11: Disentangling Content and Formal Reasoning in Large Language ModelsInternational Workshop on Semantic Evaluation (SemEval)Marco Valentino, Leonardo Ranaldi, Giulia Pucci, et al.2026 · 22 citations
20. Humans Learn Language from Situated Communicative Interactions. What about Machines?Computational LinguisticsKatrien Beuls, Paul Van Eecke2024 · 21 citations
21. Evaluating Large Language Models with NeuBAROCO: Syllogistic Reasoning Ability and Human-like BiasesarXiv:2306.12567Risako Ando, Takanobu Morishita, Hirohiko Abe, et al.2023 · 17 citations
22. Unveiling Language Competence Neurons: A Psycholinguistic Approach to Model InterpretabilityInternational Conference on Computational Linguistics (COLING)Xufeng Duan, Xinyu Zhou, Bei Xiao, et al.2025 · 8 citations
23. Semantic Change Characterization with LLMs using RhetoricsProceedings for the International Workshop on Computational Approaches to Language ChangeJáder Martins Camboim de Sá, Jooyoung Lee, Marcos Da Silveira, et al.2026 · 3 citations
24. No Context Needed: Contextual Quandary In Idiomatic Reasoning With Pre-Trained Language ModelsConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Kellen Tan Cheng, Suma Bhat2024 · 3 citations
25. AICOE-Tredence at SemEval-2026 Task 11: Mitigating Content Bias in Syllogisms via Symbolic Logic-Language DecouplingInternational Workshop on Semantic Evaluation (SemEval)Rakshith R, Ankush Chopra2026 · 1 citation
26. BIS Reasoning 1.0: The First Large-Scale Japanese Benchmark for Belief-Inconsistent Syllogistic ReasoningLanguage Resources and Evaluation Conference (LREC)Ha Thanh Nguyen, Hideyuki Tachibana, Chaoran Liu, et al.2026 · 1 citation
27. Ellat at SemEval-2026 Task 11: Comparing Encoder and Decoder Models for Syllogistic ReasoningInternational Workshop on Semantic Evaluation (SemEval)Farzaneh Bayan Memar, Hanneke Huls, Matthijs Ten Hove2026 · 1 citation
28. GIL-Zaragoza at SemEval 2026 Task 11: Comparing Classification, Autoformalization, and Ontologies for Formal Reasoning CapabilitiesInternational Workshop on Semantic Evaluation (SemEval)Francisco Lopez-Ponce, Lucia Pitarch, Iván Saavedra Martínez, et al.2026 · 1 citation
29. HABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via Synthetic Training and Multi-Objective OptimizationInternational Workshop on Semantic Evaluation (SemEval)Abdullah Shaikh, Zain Naqi, Taha Zahid, et al.2026 · 1 citation
30. Hidetsune at SemEval-2026 Task 11: Adapting Pretrained Reasoning Models with Deep Supervision and Inference Refinement for Content-Independent Validity ClassificationInternational Workshop on Semantic Evaluation (SemEval)Hidetsune Takahashi2026 · 1 citation
31. ITLC at SemEval-2026 Task 11: Normalization and Deterministic Parsing for Formal Reasoning in LLMsInternational Workshop on Semantic Evaluation (SemEval)Wicaksono Leksono Muhamad, Joanito Agili Lopo, Tack Hwa Wong, et al.2026 · 1 citation
32. Lakksh at SemEval-2026 Task 11(1 & 2): Neuro-Symbolic Decomposition to Mitigate Content Bias in Syllogistic ReasoningInternational Workshop on Semantic Evaluation (SemEval)Lakksh Sharma, Krish Sharma, Jatin Bedi2026 · 1 citation
33. ModusPonens at SemEval-2026 Task 11: Breaking the Plausibility Trap in LLMs via Conflict-Aware EnsemblingInternational Workshop on Semantic Evaluation (SemEval)Soumyajit Roy, Manav Malhotra2026 · 1 citation
34. YNU-NLP at SemEval-2026 Task 11: A Neuro-Symbolic Approach with Reflexion Mechanism Disentangling Content and Formal Reasoning in Language ModelsInternational Workshop on Semantic Evaluation (SemEval)Yu Wang, You Zhang, Hao Zhang, et al.2026 · 1 citation
35. HCMUSDroneBoys at SemEval-2026 Task 11: Asymmetric Counterfactual Debiasing and Rank-Sensitive Logical Invariance Adaptation for Syllogistic ReasoningInternational Workshop on Semantic Evaluation (SemEval)Nguyen Tran, Duy Minh Dao Sy, Trung Kiet Huynh, et al.2026
36. Thinking in Schemas: Robust Syllogistic Reasoning in LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Federico Ranaldi, Leonardo Ranaldi, Fabio Massimo Zanzotto, et al.2026
37. YNU-HPCC at SemEval-2026 Task 11: Mitigating Content Effects in Syllogistic Reasoning with Qwen2-1.5B-Instruct and XLM-RoBERTa-Large for English and Multilingual TasksMultilingual TasksInternational Workshop on Semantic Evaluation (SemEval)Rongchuan Luo, Jin Wang, Xuejie Zhang2026

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