Gorilla Benchmark API Bench
APIBench, a comprehensive dataset of over 11,000 instruction-API pairs from HuggingFace, TorchHub, and TensorHub APIs for evaluating language models' ability to generate accurate API calls.
Llama 3.1 405B Instruct from Meta currently leads the Gorilla Benchmark API Bench leaderboard with a score of 0.353 across 3 evaluated AI models.
What Gorilla Benchmark API Bench measures
Gorilla Benchmark API Bench is a text benchmark that evaluates large language models on reasoning, code, and tool calling tasks. LLM Stats tracks 3 models on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.2, with the leader reaching 0.4.
Compare leaders on the best AI for reasoning, best AI for code and best AI for tool calling leaderboards.
Publication
- Paper
- Gorilla: Large Language Model Connected with Massive APIs
- Authors
- Shishir G. Patil, Tianjun Zhang, Xin Wang, Joseph E. Gonzalez
- Published
- arXiv
- 2305.15334
Abstract
Large Language Models (LLMs) have seen an impressive wave of advances recently, with models now excelling in a variety of tasks, such as mathematical reasoning and program synthesis. However, their potential to effectively use tools via API calls remains unfulfilled. This is a challenging task even for today's state-of-the-art LLMs such as GPT-4, largely due to their inability to generate accurate input arguments and their tendency to hallucinate the wrong usage of an API call. We release Gorilla, a finetuned LLaMA-based model that surpasses the performance of GPT-4 on writing API calls. When combined with a document retriever, Gorilla demonstrates a strong capability to adapt to test-time document changes, enabling flexible user updates or version changes. It also substantially mitigates the issue of hallucination, commonly encountered when prompting LLMs directly. To evaluate the model's ability, we introduce APIBench, a comprehensive dataset consisting of HuggingFace, TorchHub, and TensorHub APIs. The successful integration of the retrieval system with Gorilla demonstrates the potential for LLMs to use tools more accurately, keep up with frequently updated documentation, and consequently increase the reliability and applicability of their outputs. Gorilla's code, model, data, and demo are available at https://gorilla.cs.berkeley.edu
Llama 3.1 405B Instruct leads with 35.3%, followed by Llama 3.1 70B Instruct at 29.7% and Llama 3.1 8B Instruct at 8.2%.
Progress Over Time
Interactive timeline showing model performance evolution on Gorilla Benchmark API Bench
Gorilla Benchmark API Bench Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | 405B | — | — | |||
| 2 | 70B | — | — | |||
| 3 | 8B | — | — |
FAQ
Common questions about Gorilla Benchmark API Bench.
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