Summary of Nestful: a Benchmark For Evaluating Llms on Nested Sequences Of Api Calls, by Kinjal Basu et al.
NESTFUL: A Benchmark for Evaluating LLMs on Nested Sequences of API Calls
by Kinjal Basu, Ibrahim Abdelaziz, Kiran Kate, Mayank Agarwal, Maxwell Crouse, Yara Rizk, Kelsey Bradford, Asim Munawar, Sadhana Kumaravel, Saurabh Goyal, Xin Wang, Luis A. Lastras, Pavan Kapanipathi
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 a new benchmark, NESTFUL, to evaluate the ability of large language models (LLMs) to perform nested sequencing, where the output of one API call is passed as input to a subsequent call. The authors present experimental results on multiple models and settings, showing that the best-performing model achieves a full sequence match accuracy of 25% and a win-rate of 34%. This highlights the complexity of this task and suggests a large scope for improvement. The paper also provides possible future research directions and releases the NESTFUL dataset under the Apache 2.0 license. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how computers can do tasks using big language models. These models are like super smart helpers that can do lots of things, but sometimes they need to use other tools or functions to get their work done. The authors made a special test to see how well these models do when they have to use multiple tools in the right order. They found that even the best models only got 25% of the tasks correct, which means there’s still lots to learn. This is important because it can help us make better computers and smart helpers for the future. |
