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Zhen Guo

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2 papers

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ICLR Conference 2025 Conference Paper

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

Zhen Guo
Adriana Meza Soria
Wei Sun
Yikang Shen
Rameswar Panda

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.

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AAAI Conference 2010 Conference Paper

A Topic Model for Linked Documents and Update Rules for its Estimation

Zhen Guo
Shenghuo Zhu
Zhongfei Zhang
Yun Chi
Yihong Gong

The latent topic model plays an important role in the unsupervised learning from a corpus, which provides a probabilistic interpretation of the corpus in terms of the latent topic space. An underpinning assumption which most of the topic models are based on is that the documents are assumed to be independent of each other. However, this assumption does not hold true in reality and the relations among the documents are available in different ways, such as the citation relations among the research papers. To address this limitation, in this paper we present a Bernoulli Process Topic (BPT) model, where the interdependence among the documents is modeled by a random Bernoulli process. In the BPT model a document is modeled as a distribution over topics that is a mixture of the distributions associated with the related documents. Although BPT aims at obtaining a better document modeling by incorporating the relations among the documents, it could also be applied to many applications including detecting the topics from corpora and clustering the documents. We apply the BPT model to several document collections and the experimental comparisons against several state-of-the-art approaches demonstrate the promising performance.

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