Summary of Seal: Suite For Evaluating Api-use Of Llms, by Woojeong Kim et al.
SEAL: Suite for Evaluating API-use of LLMs
by Woojeong Kim, Ashish Jagmohan, Aditya Vempaty
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces SEAL, an end-to-end testbed designed to evaluate large language models (LLMs) in real-world API usage. The current benchmarks have limitations, including lack of generalizability and instability due to real-time API fluctuations. SEAL standardizes existing benchmarks, integrates an agent system for testing API retrieval and planning, and addresses instability by introducing a GPT-4-powered API simulator with caching for deterministic evaluations. The 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have limitations when handling tasks requiring real-time access to external APIs. Researchers developed several benchmarks, but they suffer from issues like lack of generalizability and instability due to real-time API fluctuations. To solve this problem, scientists created SEAL, a testbed that evaluates LLMs in real-world API usage. SEAL makes existing benchmarks better, adds an agent system for testing API retrieval, and uses a simulator to make evaluations more reliable. |
Keywords
» Artificial intelligence » Gpt
