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Equitable Access to Justice: Logical LLMs Show Promise

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arxiv 2410.09904 v1 pith:AZBNMES2 submitted 2024-10-13 cs.AI cs.CYcs.LO

Manuj Kant , Manav Kant , Marzieh Nabi , Preston Carlson , Megan Ma This is my paper · ORCID
classification cs.AIcs.CYcs.LO
keywords llmsaccessjusticelegalsystemcapabilitiescontractsinsurance
verification ladder T0 review T1 audit T2 compute T3 formal

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The costs and complexity of the American judicial system limit access to legal solutions for many Americans. Large language models (LLMs) hold great potential to improve access to justice. However, a major challenge in applying AI and LLMs in legal contexts, where consistency and reliability are crucial, is the need for System 2 reasoning. In this paper, we explore the integration of LLMs with logic programming to enhance their ability to reason, bringing their strategic capabilities closer to that of a skilled lawyer. Our objective is to translate laws and contracts into logic programs that can be applied to specific legal cases, with a focus on insurance contracts. We demonstrate that while GPT-4o fails to encode a simple health insurance contract into logical code, the recently released OpenAI o1-preview model succeeds, exemplifying how LLMs with advanced System 2 reasoning capabilities can expand access to justice.

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Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Credible, Not Always Correct: How Reddit Users Verify AI-Generated Legal Advice

    cs.CY 2026-08 conditional novelty 6.0 of 10

    Most lay users on Reddit act on AI-generated legal advice without reported verification, and the advice gains force from its credible form rather than from any check on its accuracy.

  2. Revisiting Test-Time Scaling: A Survey and a Diversity-Aware Method for Efficient Reasoning

    cs.CL 2025-06 conditional novelty 5.0 of 10

    ADAPT, a diversity-aware prefix fine-tuning method, improves best-of-N sampling efficiency for a 1.5B reasoning model, reaching 80% accuracy at N=32 versus N=256 for the baseline.

  3. o1-Coder: an o1 Replication for Coding

    cs.SE 2024-11 conditional novelty 5.0 of 10

    O1-CODER is an early-stage open-source recipe for o1-style code reasoning, with a test-case generator reaching 89.2% pass rate and pseudocode search improving reasoning-path success but not final Pass@1.

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