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This paper presents a systematic human audit of NL-to-FOL datasets FOLIO and MALLS, finding 39% and 36% incorrect formalizations respectively. It releases corrected ground truths and an LLM-assisted framework to focus human relabeling, reducing the review workload to under 24% of instances for 90% accuracy.
This paper provides an algebraic formalization of the Theory of Dyadic Morality using structural causal modeling, and demonstrates applications to AI policy design.
This paper introduces LANTERN, a framework for multi-source neurosymbolic transfer in reinforcement learning that uses LLMs to generate task automata and adaptive gating to improve sample efficiency.