arXiv Computation and Language By Andrea Brunello, Cristian Curaba, Luca Geatti, Michele Mignani, Angelo Montanari, Nicola Saccomanno

Natural Language to First-Order Logic LLM-based Autoformalization

Read the original on arXiv Computation and Language →

The paper introduces a clear definition for the task of converting natural language into First‑Order Logic (FOL), separating ontology extraction from logical translation to improve evaluation consistency. It surveys current datasets, evaluation metrics, and LLM‑based approaches—including fine‑tuning, prompting, and verification‑based refinement. The authors also outline remaining challenges in benchmarking, semantic evaluation, ontology‑aware methods, and practical applications.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

arXiv Machine Learning
Jul 21

LogicIF: Towards Complex Logic Instruction Following

arXiv:2508. 09125v3 Announce Type: replace-cross Abstract: Instruction following has catalyzed the recent era of Large Language Models (LLMs) and is the foundational skill underpinning more advanced capabilities such as reasoning and agentic behaviors.

By Mian Zhang, Shujian Liu, Sixun Dong, Ming Yin, Yebowen Hu, Xun Wang, Simin Ma, Song Wang, Sathish Reddy Indurthi, Haoyun Deng, Zhiyu Zoey Chen, Kaiqiang Song
arXiv AI
Oct 23

When Form Changes but Logic Doesn't: Building Logic-invariant LLMs through Logical Structure

The paper investigates whether large language models (LLMs) truly grasp logical structure or merely rely on surface semantics. By evaluating models on logically equivalent inputs, the authors find that many LLMs fail to produce consistent predictions and struggle with unseen logical forms. They introduce LoGIST, a framework that maps inputs to decision diagrams and uses these logical embeddings to guide LLM reasoning, thereby reducing inconsistency and improving generalization.

By Xuyuan Liu, Xinshuai Dong, Elynn Chen, Yujun Yan
arXiv AI
Jul 23

Logic-Guided Data Extraction with Answer Set Programming and Large Language Models

arXiv:2607. 19365v1 Announce Type: new Abstract: When Large Language Models (LLMs) are used for semantic data extraction from unstructured text, producing candidate relational facts from natural language, they may remain unreliable for tasks requiring complex combinatorial reasoning and global consistency.

By Mario Alviano, Lorenzo Grillo, Nicola Leone, Fabrizio Lo Scudo