% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").

@article{dasgupta2024languagemodelshumanlikecontent,
    author = {Lampinen, Andrew K and Dasgupta, Ishita and Chan, Stephanie C Y and Sheahan, Hannah R and Creswell, Antonia and Kumaran, Dharshan and McClelland, James L and Hill, Felix},
    title = {Language models, like humans, show content effects on reasoning tasks},
    journal = {PNAS Nexus},
    volume = {3},
    number = {7},
    pages = {pgae233},
    year = {2024},
    month = {07},
    abstract = {Abstract reasoning is a key ability for an intelligent system. Large language models (LMs) achieve above-chance performance on abstract reasoning tasks but exhibit many imperfections. However, human abstract reasoning is also imperfect. Human reasoning is affected by our real-world knowledge and beliefs, and shows notable “content effects”; humans reason more reliably when the semantic content of a problem supports the correct logical inferences. These content-entangled reasoning patterns are central to debates about the fundamental nature of human intelligence. Here, we investigate whether language models—whose prior expectations capture some aspects of human knowledge—similarly mix content into their answers to logic problems. We explored this question across three logical reasoning tasks: natural language inference, judging the logical validity of syllogisms, and the Wason selection task. We evaluate state of the art LMs, as well as humans, and find that the LMs reflect many of the same qualitative human patterns on these tasks—like humans, models answer more accurately when the semantic content of a task supports the logical inferences. These parallels are reflected in accuracy patterns, and in some lower-level features like the relationship between LM confidence over possible answers and human response times. However, in some cases the humans and models behave differently—particularly on the Wason task, where humans perform much worse than large models, and exhibit a distinct error pattern. Our findings have implications for understanding possible contributors to these human cognitive effects, as well as the factors that influence language model performance.},
    issn = {2752-6542},
    doi = {10.1093/pnasnexus/pgae233},
    url = {https://doi.org/10.1093/pnasnexus/pgae233},
    eprint = {https://academic.oup.com/pnasnexus/article-pdf/3/7/pgae233/58651606/pgae233.pdf},
}





@article{evans1983conflict,
  title = {On the conflict between logic and belief in syllogistic reasoning},
  author = {Evans, Jonathan St BT and Barston, Julie L and Pollard, Paul},
  journal = {Memory \& Cognition},
  volume = {11},
  number = {3},
  pages = {295--306},
  year = {1983},
  publisher = {Springer}
}

@inproceedings{hua-etal-2025-disentangling,
    title = "Disentangling Logic: The Role of Context in Large Language Model Reasoning Capabilities",
    author = "Hua, Wenyue  and
      Zhu, Kaijie  and
      Li, Lingyao  and
      Fan, Lizhou  and
      Jin, Mingyu  and
      Lin, Shuhang  and
      Xue, Haochen  and
      Li, Zelong  and
      Wang, Jindong  and
      Zhang, Yongfeng",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.findings-acl.983/",
    doi = "10.18653/v1/2025.findings-acl.983",
    pages = "19219--19242",
    ISBN = "979-8-89176-256-5",
}

@inproceedings{kim-etal-2025-reasoning,
    title = "Reasoning Circuits in Language Models: A Mechanistic Interpretation of Syllogistic Inference",
    author = "Kim, Geonhee  and
      Valentino, Marco  and
      Freitas, Andre",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.findings-acl.525/",
    doi = "10.18653/v1/2025.findings-acl.525",
    pages = "10074--10095",
    ISBN = "979-8-89176-256-5",
}

@inproceedings{pan-etal-2023-logic,
    title = "Logic-{LM}: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning",
    author = "Pan, Liangming  and
      Albalak, Alon  and
      Wang, Xinyi  and
      Wang, William",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-emnlp.248/",
    doi = "10.18653/v1/2023.findings-emnlp.248",
    pages = "3806--3824",
}

@article{poddar2025understandingsyllogisticreasoningllms,
  author       = {Aheli Poddar and
                  Saptarshi Sahoo and
                  Sujata Ghosh},
  title        = {Understanding Syllogistic Reasoning in LLMs from Formal and Natural
                  Language Perspectives},
  journal      = {CoRR},
  volume       = {abs/2512.12620},
  year         = {2025},
  url          = {https://doi.org/10.48550/arXiv.2512.12620},
  doi          = {10.48550/ARXIV.2512.12620},
  eprinttype    = {arXiv},
  eprint       = {2512.12620},
  timestamp    = {Sun, 01 Feb 2026 13:37:27 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2512-12620.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
}

@article{valentino2025mitigating,
  title = {Mitigating Content Effects on Reasoning in Language Models through Fine-Grained Activation Steering},
  author = {Valentino, Marco and Kim, Geonhee and Dalal, Dhairya and Zhao, Zhixue and Freitas, Andr{\'e}},
  journal = {arXiv preprint arXiv:2505.12189},
  year = {2025}
}

@inproceedings{valentino-etal-2026-semeval, 
title = "SemEval-2026 Task 11: Disentangling Content and Formal Reasoning in Large Language Models", 
author = { Valentino, Marco and Ranaldi, Leonardo and Pucci, Giulia and Ranaldi, Federico and Freitas, Andr{\'e}}, 
booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)", 
year = "2026" }

@inproceedings{xu-etal-2024-faithful,
    title = "Faithful Logical Reasoning via Symbolic Chain-of-Thought",
    author = "Xu, Jundong  and
      Fei, Hao  and
      Pan, Liangming  and
      Liu, Qian  and
      Lee, Mong-Li  and
      Hsu, Wynne",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.acl-long.720/",
    doi = "10.18653/v1/2024.acl-long.720",
    pages = "13326--13365",
}

@inproceedings{zong-lin-2024-categorical,
    title = "Categorical Syllogisms Revisited: A Review of the Logical Reasoning Abilities of {LLM}s for Analyzing Categorical Syllogisms",
    author = "Zong, Shi  and
      Lin, Jimmy",
    editor = "Peled-Cohen, Lotem  and
      Calderon, Nitay  and
      Lissak, Shir  and
      Reichart, Roi",
    booktitle = "Proceedings of the 1st Workshop on NLP for Science (NLP4Science)",
    month = nov,
    year = "2024",
    address = "Miami, FL, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.nlp4science-1.20/",
    doi = "10.18653/v1/2024.nlp4science-1.20",
    pages = "230--239",
}



@inproceedings{ando-etal-2023-evaluating,
    title = "Evaluating Large Language Models with {N}eu{BAROCO}: Syllogistic Reasoning Ability and Human-like Biases",
    author = "Ando, Risako  and
      Morishita, Takanobu  and
      Abe, Hirohiko  and
      Mineshima, Koji  and
      Okada, Mitsuhiro",
    editor = "Chatzikyriakidis, Stergios  and
      de Paiva, Valeria",
    booktitle = "Proceedings of the 4th Natural Logic Meets Machine Learning Workshop",
    month = jun,
    year = "2023",
    address = "Nancy, France",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.naloma-1.1/",
    pages = "1--11",
    abstract = "This paper investigates whether current large language models exhibit biases in logical reasoning, similar to humans. Specifically, we focus on syllogistic reasoning, a well-studied form of inference in the cognitive science of human deduction. To facilitate our analysis, we introduce a dataset called NeuBAROCO, originally designed for psychological experiments that assess human logical abilities in syllogistic reasoning. The dataset consists of syllogistic inferences in both English and Japanese. We examine three types of biases observed in human syllogistic reasoning: belief biases, conversion errors, and atmosphere effects. Our findings demonstrate that current large language models struggle more with problems involving these three types of biases."
}