arXiv:2603. 06114v2 Announce Type: replace-cross Abstract: Real-world arguments in text and dialogues are normally enthymemes (i.
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The paper tackles the challenge of reconstructing argument graphs from natural language by addressing enthymemes—arguments with implicit premises. It proposes a neuro‑symbolic pipeline that employs large language models to generate intermediate implicit premises, translates them into logical formulas, and combines them with explicit premises and claims to determine entailment, contradiction, or neutrality. The method is evaluated on the Microtext Argumentative Corpus.
The paper presents a neuro‑symbolic approach for selecting the correct omitted component in enthymemes, extending prior work from missing‑premise to missing‑claim selection. It replaces binary entailment with logical‑resistance scores and introduces the Possible‑World Atom‑Link Formalization (PWAL), which marginalizes over alternative semantic‑link configurations while keeping translated formulae fixed. Experiments on five tasks show that PWAL improves strict accuracy by up to 30.86 percentage points and reduces tie rates significantly, while also providing a transparent trace of each comparison.
arXiv:2507. 09751v3 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but exhibit problems with logical consistency in their output.
arXiv:2609.26610v1 Announce Type: new Abstract: Despite their outstanding performance on many NLP tasks, LLMs face serious challenges related to semantic abstraction. In this study, we are interested...
arXiv:2511. 09008v2 Announce Type: replace-cross Abstract: Large Language Models perform well at natural language interpretation and reasoning, but their lack of formal correctness guarantees limits their adoption in regulated industries like finance and health-care that operate under strict policies.
arXiv:2606. 14142v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly adopted as backbones for Generative Recommendation (GR), promising access to pretrained world knowledge.