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.

通过引入辩证推理方法，使用大语言模型（LLMs）构建论证框架，从而使 LLMs 的决策能够被自然地解释和争辩，通过在声称验证的决策任务中的实验，我们证明了辩论性 LLMs 的有效性，结果竞争力强于同类技术。

用于可解释和可争议决策的论证型大型语言模型

Argumentative Large Language Models for Explainable and Contestable  Decision-Making

We evaluate the ability of contemporary large language models (LLMs) to
perform argumentative reasoning. We frame our experiments in terms of the
argument mining (AM) and argument pair extraction (APE) tasks, and evaluate
their ability to perform reasoning at increasing levels of abstraction in the
input and output representations (e.g., arbitrary label sets, semantic graphs).
We find that, although LLMs are able to match or surpass the state-of-the-art
in AM and APE, their argumentative reasoning performance is very dependent on
the input and output representation. We also find an "exemplar effect", where
too many exemplars increasingly become detrimental for task performance, and
about 4-5 being the optimal amount. Neither result extends to chain-of-thought
(CoT) prompting: we find the exemplar effect to be nullified, and our results
suggest that CoT allows for better performance under ill-conditioned problems.
We hope that the work reported contributes to the improvement of argumentative
reasoning in LLMs.

评估当代大型语言模型（LLM）进行论证推理的能力，发现大语言模型在论证挖掘（AM）和论证对提取（APE）任务中表现出了能够匹配或超过最新技术的能力，但它们的论证推理性能非常依赖于输入和输出表示形式。