Gorilla: Large Language Model Connected with Massive APIs — interactive visual explainer | Rudrite Research

Researchers Patil et al. introduced Gorilla, a LLaMA-7B model fine-tuned on machine-generated instruction-API pairs to reduce API hallucinations, outperforming GPT-4 on the APIBench benchmark. The model optionally reads retrieved documentation at inference and is evaluated using AST sub-tree matching. An interactive visual explainer of the paper is available online.

Gorilla: Large Language Model Connected with Massive APIs Stop hallucinating APIs: a LLaMA-7B fine-tuned on machine-generated instruction–API pairs, optionally reading retrieved docs at inference, and graded by AST sub-tree matching — out-calling GPT-4 on APIBench while cutting hallucinated calls. Patil et al. · arXiv 2023 · Reasoning & RL. Read the paper ↗ https://arxiv.org/abs/2305.15334 A free, interactive, animated visual explainer of Gorilla: Large Language Model Connected with Massive APIs — every exhibit computed from the real formulas, with verbatim quotes from the source. Questions - What is Gorilla: Large Language Model Connected with Massive APIs? - Stop hallucinating APIs: a LLaMA-7B fine-tuned on machine-generated instruction–API pairs, optionally reading retrieved docs at inference, and graded by AST sub-tree matching — out-calling GPT-4 on APIBench while cutting hallucinated calls. - Who published Gorilla: Large Language Model Connected with Massive APIs, and where? - Patil et al. — arXiv 2023 arXiv:2305.15334 . - Where can I find a visual explainer of Gorilla: Large Language Model Connected with Massive APIs? - Right here — a free, interactive, animated walkthrough of the whole paper, with exhibits computed from the real formulas and verbatim quotes from the source. Related explainers DeepSeek-R1 /deepseek-r1 Chain-of-Thought Prompting Elicits Reasoning in Large Language Models /chain-of-thought Training language models to follow instructions with human feedback /instructgpt Direct Preference Optimization: Your Language Model is Secretly a Reward Model /dpo DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models /deepseekmath Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters /test-time-compute Constitutional AI: Harmlessness from AI Feedback /constitutional-ai DAPO: An Open-Source LLM Reinforcement Learning System at Scale /dapo