Know Your Limits : On the Faithfulness of LLMs as Solvers and Autoformalizers in Legal Reasoning
arXiv:2606.16118v1 Announce Type: cross Abstract: Large Language Models (LLMs) achieve strong performance on reasoning tasks, but whether this reflects faithful logical inference or heuristic approximation remains unclear. We study this question in legal entailment by comparing three paradigms, including pure LLM classification, LLM-based Formal Reasoning, and solver-based Formal Reasoning using the Z3 SMT solver, on a re-annotated subset of ContractNLI across five LLMs. Our re-annotation reveals a systematic and measurable gap between pragmatic legal interpretation and strict formal entailmen
The proliferation of LLMs capable of complex reasoning tasks makes understanding their limitations, particularly faithfulness to logic, a critical and timely research area.
This research provides crucial insights into the reliability and trustworthiness of LLMs in high-stakes domains like legal reasoning, informing deployment strategies and regulatory approaches.
The findings will refine the understanding of LLM capabilities beyond superficial performance, emphasizing the distinction between heuristic approximation and faithful logical inference, which could impact their integration into automated decision-making systems.
- · Formal verification tooling developers
- · Legal tech firms integrating hybrid AI systems
- · Academics researching AI safety and alignment
- · LLM developers overstating 'reasoning' capabilities
- · Any industry relying solely on black-box LLM decisions for critical tasks
- · Pure 'classification' based LLM approaches for complex reasoning
It highlights the imperative for robust evaluation beyond accuracy metrics, focusing on the underlying reasoning process.
This could accelerate the development of hybrid AI systems combining LLM natural language understanding with formal solvers for verifiable reasoning.
It might lead to new standards or regulatory requirements for AI systems in sensitive domains, mandating demonstrable logical faithfulness.
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Read at arXiv cs.CL