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Logical Reasoning over Natural Language as Knowledge Representation: A Survey

Paper · 2023 · Zonglin Yang, Xinya Du, Rui Mao et al.

Summary

Classic AI approaches to logical reasoning store knowledge in formal languages such as first-order logic and rely on symbolic solvers. Such systems break when knowledge is missing and need experts to build their knowledge bases. This survey by Yang et al. organizes an emerging alternative, which the authors call LRNLP. In it, facts and rules stay as English text, and transformer-based language models, either fine-tuned or prompted, perform the inference. The authors first ground deductive, inductive and abductive reasoning in philosophical definitions. They then argue that the paradigm is less brittle than formal systems and avoids the symbolic-representation bottlenecks of other neuro-symbolic designs. They also claim it is more interpretable and controllable than end-to-end neural models. For each reasoning type, the survey catalogues task formulations, benchmarks and methods. It notes that recent systems are typically modular: they spend one model call per reasoning step and draw premises from a retrievable textual knowledge base. The paper closes with open problems, giving a map of the field for anyone building explainable reasoning over text.

Key points

  • Deductive reasoning is divided into four task formulations: hypothesis classification (True/False/Unknown), proof generation (the proof is a directed tree), producing proofs when some of the needed information is absent, and listing all the implications that follow.
  • Deductive benchmarks range from large synthetic sets to small human-written ones: D* (500k), PARARULE-Plus (400k), ParaRules (40k), Leap-of-thought (33k), Birds-electricity (5k), EntailmentBank (1,840), FOLIO (1,435) and ENWN (100).
  • Proof-generation methods fall into three stages:
    • single-pass systems (PRover, multiPRover, EntailmentWriter);
    • stepwise generators, mostly with heuristic search (ProofWriter, EVR, IBR, IRGR, SI, FaiRR, MetGen, SCSearch);
    • stepwise systems that add a verifier (ADGV, NLProofS, Entailer, Teachme).
  • For abduction, a fine-tuned T5-11B reaches 93% test accuracy on D*-Ab. Theory-free explanation generation is improved with knowledge from COMET or ConceptNet, or with decoding that uses both past and future context (Qin et al., 2020).
  • The survey lists several open challenges:
    • the high cost of one LLM inference per step;
    • weak robustness to out-of-distribution and adversarial inputs;
    • unreliable automatic metrics;
    • little work on probabilistic inference;
    • incomplete-information proofs, where only Sprague et al. (2022) have worked and only with a single missing premise;
    • inducing rules from raw web corpora;
    • abduction over long, realistic theories.

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

 Institutions

🎓The University of Texas at DallasRichardson · United States61 works · 5,614 citations🎓Nanyang Technological UniversitySingapore · Singapore156 works · 25,644 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 2Research subjects and languages the work is about.Reasoning · 1,426 worksKnowledge graphs & ontologies · 192 works

Cite

Cite as Zonglin Yang, Xinya Du, Rui Mao, Jinjie Ni and Erik Cambria. 2023. Logical Reasoning over Natural Language as Knowledge Representation: A Survey. arXiv:2303.12023.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2303.12023 · arXiv 2303.12023

@misc{yang2023logical,
  title = {{Logical Reasoning over Natural Language as Knowledge Representation: A Survey}},
  author = {Zonglin Yang, Xinya Du, Rui Mao, Jinjie Ni and Erik Cambria},
  year = {2023},
  doi = {10.48550/arxiv.2303.12023},
  eprint = {2303.12023},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2303.12023}
}

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