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Natural Language Reasoning, A Survey

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arxiv 2303.14725 v2 pith:UNAG6Q7X submitted 2023-03-26 cs.CL

classification cs.CL
keywords reasoninglanguagenaturalconceptuallypracticallysurveyansweringbackward
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This survey paper proposes a clearer view of natural language reasoning in the field of Natural Language Processing (NLP), both conceptually and practically. Conceptually, we provide a distinct definition for natural language reasoning in NLP, based on both philosophy and NLP scenarios, discuss what types of tasks require reasoning, and introduce a taxonomy of reasoning. Practically, we conduct a comprehensive literature review on natural language reasoning in NLP, mainly covering classical logical reasoning, natural language inference, multi-hop question answering, and commonsense reasoning. The paper also identifies and views backward reasoning, a powerful paradigm for multi-step reasoning, and introduces defeasible reasoning as one of the most important future directions in natural language reasoning research. We focus on single-modality unstructured natural language text, excluding neuro-symbolic techniques and mathematical reasoning.

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Cited by 2 Pith papers

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    A multi-agent LLM framework builds AI model pipelines from ambiguous natural-language queries, improving exact-match accuracy from 15.7% to 25.2% on a 441-example benchmark.

  2. SR-FoT: A Syllogistic-Reasoning Framework of Thought for Large Language Models Tackling Knowledge-based Reasoning Tasks

    cs.AI 2025-01 conditional novelty 5.0 of 10

    A syllogism-style, five-stage prompting framework improves LLM accuracy on knowledge-based QA over chain-of-thought baselines.

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