arXiv AI

Identifying Implicit Premises for Logical Reconstruction of Argument Graphs

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.

arXiv AI
Jun 16

VeriGraph: Towards Verifiable Data-Analytic Agents

arXiv:2606. 16603v1 Announce Type: cross Abstract: LLM-based agents have demonstrated strong capabilities in data-intensive analytical tasks, yet their outputs are rarely verifiable: a reliance on linear text trajectories makes their reasoning difficult to audit.

By Jiajie Jin, Zhao Yang, Wenle Liao, Yuyang Hu, Guanting Dong, Xiaoxi Li, Yutao Zhu, Zhicheng Dou
arXiv AI
Jul 15

A Neurosymbolic Approach to Natural Language Formalization and Verification

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.

By Chenyang An, Sam Bayless, Stefano Buliani, Darion Cassel, Byron Cook, Duncan Clough, R\'emi Delmas, Nafi Diallo, Ferhat Erata, Nick Feng, Dimitra Giannakopoulou, Aman Goel, Aditya Gokhale, Joe Hendrix, Victor Heorhiadi, Marc Hudak, Dejan Jovanovi\'c, Andrew M. Kent, Benjamin Kiesl-Reiter, Jeffrey J. Kuna, Nadia Labai, Joseph Lilien, Divya Raghunathan, Zvonimir Rakamari\'c, Niloofar Razavi, Michael Tautschnig, Ali Torkamani, Nathaniel Weir, Michael W. Whalen, Jianan Yao
arXiv Computation and Language
4d ago

On the LLM Verification for Argument Graph Generation

The paper proposes a layered verification approach for argument graphs generated by large language models (LLMs). It suggests starting with deterministic checks, then using solver-based methods, followed by LLM reviews, and resorting to debate only when uncertainty remains, reserving human judgment for cases no other instrument can resolve. A study on human-annotated graphs with injected defects shows that while LLM reviewers can detect structural issues with high recall, they also produce false positives, exhibit inconsistent verdicts, and are slower than exact checks, underscoring the need for deterministic verification gates.

By Jakub B\k{a}ba, Jaros{\l}aw A. Chudziak
arXiv AI
Aug 20

Pairwise Logical Selection of Enthymeme Completions under Semantic-Link Uncertainty

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.

By Xuyao Feng, Antonis Bikakis