Summary of Equitable Access to Justice: Logical Llms Show Promise, by Manuj Kant et al.
Equitable Access to Justice: Logical LLMs Show Promise
by Manuj Kant, Manav Kant, Marzieh Nabi, Preston Carlson, Megan Ma
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY); Logic in Computer Science (cs.LO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The integration of Large Language Models (LLMs) with logic programming enhances their ability to reason, bridging the gap between AI and legal expertise. This paper explores the potential of combining LLMs with logic programming to improve access to justice, focusing on translating laws and contracts into logical code applicable to specific cases, such as insurance contracts. The study demonstrates that while GPT-4o fails to encode a simple health insurance contract into logical code, the OpenAI o1-preview model succeeds, showcasing the capabilities of LLMs with advanced System 2 reasoning in expanding access to justice. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) have the potential to improve access to legal solutions for many Americans. A major challenge is using AI and LLMs in legal contexts where consistency and reliability are crucial. This paper explores how combining LLMs with logic programming can enhance their ability to reason, making them more like a skilled lawyer. The goal is to translate laws and contracts into logical code that can be applied to specific cases, such as insurance contracts. The study shows that while GPT-4o fails, the OpenAI o1-preview model succeeds in encoding a simple health insurance contract into logical code. |
Keywords
» Artificial intelligence » Gpt
