arxiv Preprint – Gorilla: Large Language Model Connected with Massive APIs

In this episode we discuss Gorilla: Large Language Model Connected with Massive APIs
by Shishir G. Patil, Tianjun Zhang, Xin Wang, Joseph E. Gonzalez. The paper introduces Gorilla, a fine-tuned Large Language Model (LLM) that excels in generating accurate API calls. By combining Gorilla with a document retriever, the model exhibits the ability to adapt to changes in test-time documents, addressing the issue of hallucination commonly observed in LLMs. The authors introduce APIBench, a dataset containing HuggingFace, TorchHub, and TensorHub APIs, to evaluate Gorilla’s performance and demonstrate the potential for LLMs to utilize tools more accurately and enhance the reliability of their outputs.