Autoformalization and Deductive Reasoning
Translating natural-language statements into formal representations, and studying where LLMs succeed and fail once knowledge is made explicit.
Formal representations promise precision and verifiability, but only if the translation from natural language is faithful. This thread studies where that translation breaks down — in deductive reasoning, in temporal reasoning, and in the general problem of converting human statements into logic, programs, or structured queries.
Our ECAI 2025 paper Investigating the Robustness of Deductive Reasoning with Large Language Models tests how small prompt and input variations affect deductive reasoning, characterises common failure modes, and proposes an evaluation setup for robustness (Hoppe et al., 2025). Our ACL 2025 paper ChronoSense examines temporal understanding using Allen-style event intervals, highlighting current gaps in interval reasoning (Islakoglu & Kalo, 2025). Ongoing supervision work extends this line to GRPO-based autoformalization from natural language to first-order logic.