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Recent research has demonstrated that Large Language Models (LLMs) canenhance their capabilities by utilizing external tools. However, three pivotalquestions remain unanswered: (1) How effective are current LLMs in utilizingtools? (2) How can we enhance LLMs' ability to utilize tools? (3) Whatobstacles need to be overcome to leverage tools? To address these questions, weintroduce API-Bank, a groundbreaking benchmark, specifically designed fortool-augmented LLMs. For the first question, we develop a runnable evaluationsystem consisting of 73 API tools. We annotate 314 tool-use dialogues with 753API calls to assess the existing LLMs' capabilities in planning, retrieving,and calling APIs. For the second question, we construct a comprehensivetraining set containing 1,888 tool-use dialogues from 2,138 APIs spanning 1,000distinct domains. Using this dataset, we train Lynx, a tool-augmented LLMinitialized from Alpaca. Experimental results demonstrate that GPT-3.5 exhibitsimproved tool utilization compared to GPT-3, while GPT-4 excels in planning.However, there is still significant potential for further improvement.Moreover, Lynx surpasses Alpaca's tool utilization performance by more than 26pts and approaches the effectiveness of GPT-3.5. Through error analysis, wehighlight the key challenges for future research in this field to answer thethird question.

API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs