The reviewed record of science sign in
Pith

arxiv: 2402.09615 · v6 · pith:LJQ4GLHR · submitted 2024-02-14 · cs.CL · cs.AI· cs.LG

API Pack: A Massive Multi-Programming Language Dataset for API Call Generation

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:LJQ4GLHRrecord.jsonopen to challenge →

classification cs.CL cs.AIcs.LG
keywords packdatasetfine-tuninglanguagegenerationcallcallscode
0
0 comments X
read the original abstract

We introduce API Pack, a massive multi-programming language dataset containing over one million instruction-API calls for improving the API call generation capabilities of large language models. Our evaluation highlights three key findings: First, fine-tuning on API Pack enables open-source models to outperform GPT-3.5 and GPT-4 in generating code for entirely new API calls. We show this by fine-tuning CodeLlama-13B on 20,000 Python instances from API Pack. Second, fine-tuning on a large dataset in one language, combined with smaller datasets from others, improves API generation accuracy across multiple languages. Third, we confirm the benefits of larger datasets for API generalization, as increasing fine-tuning data to one million instances enhances generalization to new APIs. To support further research, we open-source the API Pack dataset, trained model, and code at https://github.com/zguo0525/API-Pack.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Intent2Tx: Benchmarking LLMs for Translating Natural Language Intents into Ethereum Transactions

    cs.AI 2026-04 unverdicted novelty 7.0

    Intent2Tx shows that LLMs often generate syntactically valid but functionally incorrect Ethereum transactions, especially on multi-step and out-of-distribution intents, despite gains from scaling and retrieval augmentation.