2024/09/24 by Zhuocheng Shen, Shen, Zhuocheng · 15 citations
Computer Science · #Artificial Intelligence (cs.AI) #Digital Rights Management and Security #FOS: Computer and information sciences #Semantic Web and Ontologies
paper · pdf · doi:10.48550/arxiv.2409.18807
openalex publication_date 2024/09/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The integration of tools in augmenting large language models presents a novel approach toward enhancing the efficiency and accuracy of these models in handling specific, complex tasks. This paper delves into the methodology,challenges, and developments in the realm of teaching LLMs to use external tools, thereby pushing the boundaries of their capabilities beyond pre-existing knowledge bases. We introduce a standardized paradigm for tool integration guided by a series of functions that map user instructions to actionable plans and their execution, emphasizing the significance of understanding user intent, tool selection, and dynamic plan adjustment. Our exploration reveals the various challenges encountered, such as tool invocation timing, selection accuracy, and the need for robust reasoning processes. In addressing these challenges, we investigate techniques within the context of fine-tuning and incontext learning paradigms, highlighting innovative approaches to ensure diversity, augment datasets, and improve generalization.Furthermore, we investigate a perspective on enabling LLMs to not only utilize but also autonomously create tools, which may redefine their role from mere tool users to tool creators. Finally,we reproduced Chameleon's results on ScienceQA and analyzed the code structure.