Selecting from Multiple Strategies Improves the Foreseeable Reasoning of Tool-Augmented Large Language Models

Yongchao Wu, Aron Henriksson

Abstract: Large language models (LLMs) can be augmented by interacting with external tools and knowledge bases, allowing them to overcome some of their known limitations, such as not having access to up-to-date information or struggling to solve math problems, thereby going beyond the knowledge and capabilities obtained during pre-training. Recent prompting techniques have enabled tool-augmented LLMs to combine reasoning and action to solve complex problems with the help of tools. This is essential for allowing LLMs to strategically determine the timing and nature of tool-calling actions in order to enhance their decision-making process and improve their outputs. However, the reliance of current prompting techniques on a single reasoning path or their limited ability to adjust plans within that path can adversely impact the performance of tool-augmented LLMs. In this paper, we introduce a novel prompting method, whereby an LLM agent selects and executes one among multiple candidate strategies. We assess the effectiveness of our method on three question answering datasets, on which it outperforms state-of-the-art methods like ReWOO, while also being a competitive and more cost-efficient alternative to ReAct. We also investigate the impact of selecting a reasoning trajectory from different strategy pool sizes, further highlighting the risks in only considering a single strategy.

External IDs:dblp:conf/pkdd/WuH24