@inproceedings{niwa-etal-2025-rectifying,
    title = "Rectifying Belief Space via Unlearning to Harness {LLM}s' Reasoning",
    author = "Niwa, Ayana  and
      Kaneko, Masahiro  and
      Inui, Kentaro",
    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://preview.aclanthology.org/ingest-emnlp/2025.findings-acl.1285/",
    doi = "10.18653/v1/2025.findings-acl.1285",
    pages = "25060--25075",
    ISBN = "979-8-89176-256-5",
    abstract = "Large Language Models (LLMs) exhibit sophisticated reasoning yet still generate incorrect answers. We attribute these errors to **Spurious Beliefs**, defined as propositions the model internally considers as true despite being factually false. To reduce reasoning errors, we propose a belief space rectification framework. Our method first identifies the beliefs invoked during inference via an explanation{-}based approach with Forward{-}Backward Beam Search (FBBS). We subsequently apply unlearning via gradient ascent to suppress spurious beliefs and enhance true ones, thereby effectively rectifying the model{'}s belief space. Experiments on three QA datasets and three LLMs show that our method significantly reduces erroneous reasoning and improves generalization."
}