Argumentative Reasoning with Language Models on Non-factorized Case Bases

TL;DR

This paper introduces AAM-CBR, a novel framework that enables language models to perform case-based reasoning on non-factorized case bases, enhancing flexibility and privacy in reasoning processes.

Contribution

The paper presents AAM-CBR, a new framework extending abstract argumentation for case-based reasoning, allowing reasoning without factorizing previous cases and improving privacy.

Findings

AAM-CBR outperforms the baseline with richer case factors.

Language models handle few factors well but struggle with many.

Integrating symbolic reasoning is key for complex cases.

Abstract

In this paper, we investigate how language models can perform case-based reasoning (CBR) on non-factorized case bases. We introduce a novel framework, argumentative agentic models for case-based reasoning (AAM-CBR), which extends abstract argumentation for case-based reasoning (AA-CBR). Unlike traditional approaches that require factorization of previous cases, AAM-CBR leverages language models to determine case coverage and extract factors based on new cases. This enables factor-based reasoning without exposing or preprocessing previous cases, thus improving both flexibility and privacy. We also present initial experiments to assess AAM-CBR performance by comparing the proposed framework with a baseline that uses a single-prompt approach to incorporate both new and previous cases. The experiments are conducted based on a synthetic credit card application dataset. The result shows that AAM-CBR surpasses the baseline only when the new case contains a richer set of factors. The finding indicates that language models can handle case-based reasoning with a limited number of factors, but face challenges as the number of factors increase. Consequently, integrating symbolic reasoning with language models, as implemented in AAM-CBR, is crucial for effectively handling cases involving many factors.