In-context decision-making is an important capability of artificial general intelligence, which Large Language Models (LLMs) have effectively demonstrated in various scenarios. However, LLMs often face challenges when dealing with numerical contexts, and limited attention has been paid to evaluating their performance through preference feedback generated by the environment. This paper investigates the performance of LLMs as decision-makers in the context of Dueling Bandits (DB). We first evaluate the performance of LLMs by comparing GPT-3.5-Turbo, GPT-4, and GPT-4-Turbo against established DB algorithms. Our results reveal that LLMs, particularly GPT-4 Turbo, quickly identify the Condorcet winner, thus outperforming existing state-of-the-art algorithms in terms of weak regret. Nevertheless, LLMs struggle to converge even when explicitly prompted to do so, and are sensitive to prompt variations. To overcome these issues, we introduce an LLM-augmented algorithm, IF-Enhanced LLM, which takes advantage of both in-context decision-making capabilities of LLMs and theoretical guarantees inherited from classic DB algorithms. The design of such an algorithm sheds light on how to enhance trustworthiness for LLMs used in decision-making tasks where performance robustness matters. We show that IF-Enhanced LLM has theoretical guarantees on both weak and strong regret. Our experimental results validate that IF-Enhanced LLM is robust even with noisy and adversarial prompts.

在Dueling Bandits情境中，本文研究了Large Language Models (LLMs)作为决策者的表现，并引入了一个增强算法IF-Enhanced LLM，该算法结合了LLMs的上下文决策能力和经典DB算法的理论保证，以提高LLMs在做决策任务时的可信度和性能鲁棒性。

超越数值奖励：带有LLM代理的上下文对决