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2024

Equitable Access to Justice: Logical LLMs Show Promise

Kant, Manuj, Kant, Manav, Nabi, Marzieh et al.

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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.

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Judicial Council of California. (2019), Handling Cases Involving Self-Represented Litigants,
2019
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Self-Represented Litigation Network. (2021)
2021
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Eric Zelikman, Yuhuai Wu, Jesse Mu, &Goodman, N. (2022). STaR: Bootstrapping Reasoning With Reasoning
Original
2022
Earlier work this paper cites.
Cummins, J., Clack, C. D. (2022). Transforming commercial contracts through computable contracting
2022
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Li, Z., Liu, H., Zhou D., & Ma, T. (2023) Chain of Thought Empowers Transformers to Solve Inherently Serial Problems
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2023
Earlier work this paper cites.

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Then

Besta, M., Blach, N., Kubicek1, A., Gerstenberger, R., Podstawski, M., Gianinazzi, L., Gajda, J., Lehmann, T., Niewiadomski, H., Nyczyk, P., & Hoefler, T. (2024) Graph of Thoughts: Solving Elaborate Problems with Large Language Models
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