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Fact-Checking Complex Claims with Program-Guided Reasoning

Summary

Verifying real-world claims often means combining several pieces of evidence through multi-step reasoning. Yet most claims in popular datasets such as FEVER can be checked against a single article, and earlier multi-hop models are either hard to interpret or need large amounts of labeled data. The authors propose ProgramFC, a few-shot neuro-symbolic framework. A large language model (Codex) is prompted with a few dozen claim–program examples and writes a reasoning program for each new claim. The program is a sequence of steps such as Question, Verify and Predict. An interpreter then runs these steps one by one, sending each to a dedicated handler: a QA model, a simple-claim fact checker or a logical reasoner. Because the handlers can be swapped, the same pipeline works with gold evidence, with retrieval from Wikipedia (open-book) or with model knowledge alone (closed-book). On HOVER and FEVEROUS, ProgramFC beats seven few-shot baselines, and its advantage grows as claims need more reasoning hops. The programs also improve evidence retrieval and give readable explanations. This makes the approach useful for building transparent fact-checkers with little training data.

Key points

  • Method: Codex (code-davinci-002) generates the programs through in-context learning with no extra training. Each step names a function, its argument and an output variable, and the final step must return TRUE or FALSE.
  • Evaluation: The system was tested on HOVER (2-, 3- and 4-hop claims) and FEVEROUS against seven baselines. Baselines include FLAN-T5, Codex and 175B InstructGPT (text-davinci-002) with direct, CoT, ZS-CoT and Self-Ask prompting.
  • Closed-book results: Most models score only slightly above chance on HOVER. Chain-of-thought averages 2.7 points over direct prompting and beats ProgramFC on HOVER 2-hop and FEVEROUS, but falls behind it on 3-hop and 4-hop claims.
  • Limitations:
    • The tested claims mostly require explicit, syntax-driven decomposition, and the generator struggled on claims whose reasoning is implicit.
    • Run time is about 4–5× that of an end-to-end FLAN-T5 model.
  • Availability: Code and data are released at github.com/mbzuai-nlp/ProgramFC.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing

Cited by

89 papers of our library cite it (of the 140884 whose bibliographies we read); 3 of them have a page here, 86 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2023.acl-long.386

@inproceedings{pan2023fact,
  title = {{Fact-Checking Complex Claims with Program-Guided Reasoning}},
  author = {Pan, Liangming and Wu, Xiaobao and Lu, Xinyuan and Luu, Anh Tuan and Wang, William Yang and Kan, Min-Yen and Nakov, Preslav},
  booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2023},
  doi = {10.18653/v1/2023.acl-long.386},
  url = {https://doi.org/10.18653/v1/2023.acl-long.386}
}

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