Enhancing Function-Calling Capabilities in LLMs: Strategies for Prompt Formats, Data Integration, and Multilingual Translation

• URL: http://arxiv.org/abs/2412.01130v2
• Date: Wed, 04 Dec 2024 03:34:42 GMT
• Title: Enhancing Function-Calling Capabilities in LLMs: Strategies for Prompt Formats, Data Integration, and Multilingual Translation
• Authors: Yi-Chang Chen, Po-Chun Hsu, Chan-Jan Hsu, Da-shan Shiu,
• Abstract summary: Large language models (LLMs) have significantly advanced autonomous agents, particularly in function calling. This research delves into enhancing the function-calling capabilities of LLMs by exploring different approaches.
• Score: 15.259077785780667
• License:
• Abstract: Large language models (LLMs) have significantly advanced autonomous agents, particularly in zero-shot tool usage, also known as function calling. This research delves into enhancing the function-calling capabilities of LLMs by exploring different approaches, including prompt formats for integrating function descriptions, blending function-calling and instruction-following data, introducing a novel Decision Token for conditional prompts, leveraging chain-of-thought reasoning, and overcoming multilingual challenges with a translation pipeline. Our key findings and contributions are as follows: (1) Instruction-following data improves both function-calling accuracy and relevance detection. (2) The use of the newly proposed Decision Token, combined with synthetic non-function-call data, enhances relevance detection. (3) A tailored translation pipeline effectively overcomes multilingual limitations, demonstrating significant improvements in Traditional Chinese. These insights highlight the potential for improved function-calling capabilities and multilingual applications in LLMs.

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