NESTFUL: A Benchmark for Evaluating LLMs on Nested Sequences of API Calls

• URL: http://arxiv.org/abs/2409.03797v1
• Date: Wed, 4 Sep 2024 17:53:24 GMT
• Title: NESTFUL: A Benchmark for Evaluating LLMs on Nested Sequences of API Calls
• Authors: Kinjal Basu, Ibrahim Abdelaziz, Kelsey Bradford, Maxwell Crouse, Kiran Kate, Sadhana Kumaravel, Saurabh Goyal, Asim Munawar, Yara Rizk, Xin Wang, Luis Lastras, Pavan Kapanipathi,
• Abstract summary: We present NESTFUL, a benchmark to evaluate large language models (LLMs) on nested sequences of API calls. Our results show that most models do not perform well on nested APIs in NESTFUL as compared to their performance on the simpler problem settings available in existing benchmarks.
• Score: 18.831512738668792
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autonomous agent applications powered by large language models (LLMs) have recently risen to prominence as effective tools for addressing complex real-world tasks. At their core, agentic workflows rely on LLMs to plan and execute the use of tools and external Application Programming Interfaces (APIs) in sequence to arrive at the answer to a user's request. Various benchmarks and leaderboards have emerged to evaluate an LLM's capabilities for tool and API use; however, most of these evaluations only track single or multiple isolated API calling capabilities. In this paper, we present NESTFUL, a benchmark to evaluate LLMs on nested sequences of API calls, i.e., sequences where the output of one API call is passed as input to a subsequent call. NESTFUL has a total of 300 human annotated samples divided into two types - executable and non-executable. The executable samples are curated manually by crawling Rapid-APIs whereas the non-executable samples are hand picked by human annotators from data synthetically generated using an LLM. We evaluate state-of-the-art LLMs with function calling abilities on NESTFUL. Our results show that most models do not perform well on nested APIs in NESTFUL as compared to their performance on the simpler problem settings available in existing benchmarks.

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