Summary of Api-blend: a Comprehensive Corpora For Training and Benchmarking Api Llms, by Kinjal Basu et al.
API-BLEND: A Comprehensive Corpora for Training and Benchmarking API LLMs
by Kinjal Basu, Ibrahim Abdelaziz, Subhajit Chaudhury, Soham Dan, Maxwell Crouse, Asim Munawar, Sadhana Kumaravel, Vinod Muthusamy, Pavan Kapanipathi, Luis A. Lastras
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper addresses a pressing need in Large Language Models (LLMs) to effectively utilize tools and external Application Programming Interfaces (APIs) to accomplish tasks. The authors focus on curating and transforming existing datasets, introducing the API-BLEND dataset for training and testing tool-augmented LLMs. The dataset mimics real-world scenarios involving API-tasks such as detection, slot filling, and sequencing of APIs. By leveraging this dataset, researchers can train and benchmark models that better interact with tools and APIs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers learn to work with other software tools and online services. Right now, these computers (called Large Language Models) struggle to use these tools effectively. The authors are trying to fix this by creating a big collection of data that shows how tools and APIs can be used together. This “API-BLEND” dataset will help train computers to understand how to work with these tools in real-life scenarios. |
