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Beyond Static Evaluation: A Dynamic Approach to Assessing AI Assistants' API Invocation Capabilities
March 19, 2024, 4:53 a.m. | Honglin Mu, Yang Xu, Yunlong Feng, Xiaofeng Han, Yitong Li, Yutai Hou, Wanxiang Che
cs.CL updates on arXiv.org arxiv.org
Abstract: With the rise of Large Language Models (LLMs), AI assistants' ability to utilize tools, especially through API calls, has advanced notably. This progress has necessitated more accurate evaluation methods. Many existing studies adopt static evaluation, where they assess AI assistants' API call based on pre-defined dialogue histories. However, such evaluation method can be misleading, as an AI assistant might fail in generating API calls from preceding human interaction in real cases. Instead of the resource-intensive …
abstract advanced ai assistants api arxiv assistants beyond call capabilities cs.cl dynamic evaluation language language models large language large language models llms progress studies through tools type
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