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title: "Who cites Language models show — 112 citing papers"
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Language models show human-like content effects…](https://md.research.elamur.ai/free/en/papers/dasgupta-2022-language-models-show-human-like/) / cited by

# 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»](https://md.research.elamur.ai/free/en/papers/dasgupta-2022-language-models-show-human-like/)

1. **[The Rise and Potential of Large Language Model Based Agents: A Survey](https://md.research.elamur.ai/free/en/papers/xi-2023-rise-potential-large-language/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Zhiheng Xi, Wenxiang Chen, Xin Guo, et al.*2023 · 1,272 citations*
2. **[Towards Reasoning in Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/huang-2023-reasoning-large-language-models-survey/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jie Huang, Kevin Chen-Chuan Chang*2023 · 780 citations*
3. **[Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting](https://md.research.elamur.ai/free/en/papers/turpin-2023-language-models-don-t-always/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Miles Turpin, Julian Michael, Ethan Perez, et al.*2023 · 586 citations*
4. [**Augmented Language Models: a Survey arXiv:2302.07842**Grégoire Mialon, Roberto Dessì, Maria Lomeli, et al.*2023 · 369 citations*](https://md.research.elamur.ai/free/en/papers/mialon-2023-augmented-language-models-survey/)
5. [**Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought arXiv:2210.01240**Abulhair Saparov, He He*2022 · 244 citations*](https://md.research.elamur.ai/free/en/papers/saparov-2022-language-models-greedy-reasoners-systematic/)
6. [**FOLIO: Natural Language Reasoning with First-Order Logic arXiv:2209.00840**Simeng Han, Hailey Schoelkopf, Yilun Zhao, et al.*2022 · 219 citations*](https://md.research.elamur.ai/free/en/papers/han-2022-folio-natural-language-reasoning-first/)
7. **[Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models](https://md.research.elamur.ai/free/en/papers/levy-2024-same-task-more-tokens-impact/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mosh Levy, Alon Jacoby, Yoav Goldberg*2024 · 217 citations*
8. **[Sources of Hallucination by Large Language Models on Inference Tasks](https://md.research.elamur.ai/free/en/papers/mckenna-2023-sources-hallucination-large-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Nick McKenna, Tianyi Li, Liang Cheng, et al.*2023 · 212 citations*
9. **[PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits](https://md.research.elamur.ai/free/en/papers/jiang-2023-personallm-investigating-ability-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hang Jiang, Xiajie Zhang, Xubo Cao, et al.*2023 · 211 citations*
10. **[Evaluating and Inducing Personality in Pre-trained Language Models](https://md.research.elamur.ai/free/en/papers/jiang-2022-evaluating-inducing-personality-pre-trained/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**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 Design](https://md.research.elamur.ai/free/en/papers/tjuatja-2024-do-llms-exhibit-human-like/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/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 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*
13. **[Natural Language Reasoning, A Survey](https://md.research.elamur.ai/free/en/papers/yu-2023-natural-language-reasoning-survey/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Fei Yu, Hongbo Zhang, Prayag Tiwari, et al.*2023 · 130 citations*
14. [**Faithful Reasoning Using Large Language Models arXiv:2208.14271**Antonia Creswell, Murray Shanahan*2022 · 122 citations*](https://md.research.elamur.ai/free/en/papers/creswell-2022-faithful-reasoning-using-large-language/)
15. **[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*
16. **[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*
17. [**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/)
18. [**Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models arXiv:2310.00836**Man Luo, Shrinidhi Kumbhar, Ming shen, et al.*2023 · 27 citations*](https://md.research.elamur.ai/free/en/papers/luo-2023-towards-logiglue-brief-survey-benchmark/)
19. **[SemEval-2026 Task 11: Disentangling Content and Formal Reasoning in Large Language Models](https://md.research.elamur.ai/free/en/papers/valentino-2026-semeval-2026-task-11-disentangling/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Marco Valentino, Leonardo Ranaldi, Giulia Pucci, et al.*2026 · 22 citations*
20. **[Humans Learn Language from Situated Communicative Interactions. What about Machines?](https://md.research.elamur.ai/free/en/papers/beuls-2024-humans-learn-language-situated-communicative/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Katrien Beuls, Paul Van Eecke*2024 · 21 citations*
21. [**Evaluating Large Language Models with NeuBAROCO: Syllogistic Reasoning Ability and Human-like Biases arXiv:2306.12567**Risako Ando, Takanobu Morishita, Hirohiko Abe, et al.*2023 · 17 citations*](https://md.research.elamur.ai/free/en/papers/ando-2023-evaluating-large-language-models-neubaroco/)
22. **[Unveiling Language Competence Neurons: A Psycholinguistic Approach to Model Interpretability](https://md.research.elamur.ai/free/en/papers/duan-2025-unveiling-language-competence-neurons-psycholinguistic/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Xufeng Duan, Xinyu Zhou, Bei Xiao, et al.*2025 · 8 citations*
23. **[Semantic Change Characterization with LLMs using Rhetorics](https://md.research.elamur.ai/free/en/papers/sa-2026-semantic-change-characterization-llms-rhetorics/) [Proceedings for the International Workshop on Computational Approaches to Language Change](https://md.research.elamur.ai/free/en/venues/lchange/)**Jáder 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 Models](https://md.research.elamur.ai/free/en/papers/cheng-2024-no-context-needed-contextual-quandary/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Kellen Tan Cheng, Suma Bhat*2024 · 3 citations*
25. **[AICOE-Tredence at SemEval-2026 Task 11: Mitigating Content Bias in Syllogisms via Symbolic Logic-Language Decoupling](https://md.research.elamur.ai/free/en/papers/r-2026-aicoe-tredence-semeval-2026-task/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Rakshith R, Ankush Chopra*2026 · 1 citation*
26. **[BIS Reasoning 1.0: The First Large-Scale Japanese Benchmark for Belief-Inconsistent Syllogistic Reasoning](https://md.research.elamur.ai/free/en/papers/nguyen-2026-bis-reasoning-1-0-first/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/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 Reasoning](https://md.research.elamur.ai/free/en/papers/memar-2026-ellat-semeval-2026-task-11/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Farzaneh Bayan Memar, Hanneke Huls, Matthijs Ten Hove*2026 · 1 citation*
28. **[GIL-Zaragoza at SemEval 2026 Task 11: Comparing Classification, Autoformalization, and Ontologies for Formal Reasoning Capabilities](https://md.research.elamur.ai/free/en/papers/ponce-2026-gil-zaragoza-semeval-2026-task/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/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 Optimization](https://md.research.elamur.ai/free/en/papers/shaikh-2026-habib-taz-semeval-2026-task/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/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 Classification](https://md.research.elamur.ai/free/en/papers/takahashi-2026-hidetsune-semeval-2026-task-11/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Hidetsune Takahashi*2026 · 1 citation*
31. **[ITLC at SemEval-2026 Task 11: Normalization and Deterministic Parsing for Formal Reasoning in LLMs](https://md.research.elamur.ai/free/en/papers/muhamad-2026-itlc-semeval-2026-task-11/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/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 Reasoning](https://md.research.elamur.ai/free/en/papers/sharma-2026-lakksh-semeval-2026-task-11/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Lakksh Sharma, Krish Sharma, Jatin Bedi*2026 · 1 citation*
33. **[ModusPonens at SemEval-2026 Task 11: Breaking the Plausibility Trap in LLMs via Conflict-Aware Ensembling](https://md.research.elamur.ai/free/en/papers/roy-2026-modusponens-semeval-2026-task-11/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Soumyajit Roy, Manav Malhotra*2026 · 1 citation*
34. **[YNU-NLP at SemEval-2026 Task 11: A Neuro-Symbolic Approach with Reflexion Mechanism Disentangling Content and Formal Reasoning in Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-ynu-nlp-semeval-2026-task/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/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 Reasoning](https://md.research.elamur.ai/free/en/papers/tran-2026-hcmusdroneboys-semeval-2026-task-11/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Nguyen Tran, Duy Minh Dao Sy, Trung Kiet Huynh, et al.*2026*
36. **[Thinking in Schemas: Robust Syllogistic Reasoning in LLMs](https://md.research.elamur.ai/free/en/papers/ranaldi-2026-thinking-schemas-robust-syllogistic-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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 Tasks](https://md.research.elamur.ai/free/en/papers/luo-2026-ynu-hpcc-semeval-2026-task/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Rongchuan Luo, Jin Wang, Xuejie Zhang*2026*

75 more papers of our library cite it; they have no page here yet.

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