SEAL: Suite for Evaluating API-use of LLMs

• URL: http://arxiv.org/abs/2409.15523v1
• Date: Mon, 23 Sep 2024 20:16:49 GMT
• Title: SEAL: Suite for Evaluating API-use of LLMs
• Authors: Woojeong Kim, Ashish Jagmohan, Aditya Vempaty,
• Abstract summary: SEAL is an end-to-end testbed designed to evaluate large language models in real-world API usage. It standardizes existing benchmarks, integrates an agent system for testing API retrieval and planning, and addresses the instability of real-time APIs.
• Score: 1.2528321519119252
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have limitations in handling tasks that require real-time access to external APIs. While several benchmarks like ToolBench and APIGen have been developed to assess LLMs' API-use capabilities, they often suffer from issues such as lack of generalizability, limited multi-step reasoning coverage, and instability due to real-time API fluctuations. In this paper, we introduce SEAL, an end-to-end testbed designed to evaluate LLMs in real-world API usage. SEAL standardizes existing benchmarks, integrates an agent system for testing API retrieval and planning, and addresses the instability of real-time APIs by introducing a GPT-4-powered API simulator with caching for deterministic evaluations. Our testbed provides a comprehensive evaluation pipeline that covers API retrieval, API calls, and final responses, offering a reliable framework for structured performance comparison in diverse real-world scenarios. SEAL is publicly available, with ongoing updates for new benchmarks.

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