Argumentative Large Language Models for Explainable and Contestable Decision-Making
arxiv(2024)
摘要
The diversity of knowledge encoded in large language models (LLMs) and their
ability to apply this knowledge zero-shot in a range of settings makes them a
promising candidate for use in decision-making. However, they are currently
limited by their inability to reliably provide outputs which are explainable
and contestable. In this paper, we attempt to reconcile these strengths and
weaknesses by introducing a method for supplementing LLMs with argumentative
reasoning. Concretely, we introduce argumentative LLMs, a method utilising LLMs
to construct argumentation frameworks, which then serve as the basis for formal
reasoning in decision-making. The interpretable nature of these argumentation
frameworks and formal reasoning means that any decision made by the
supplemented LLM may be naturally explained to, and contested by, humans. We
demonstrate the effectiveness of argumentative LLMs experimentally in the
decision-making task of claim verification. We obtain results that are
competitive with, and in some cases surpass, comparable state-of-the-art
techniques.
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