{"slug": "gorilla-large-language-model-connected-with-massive-apis-interactive-visual", "title": "Gorilla: Large Language Model Connected with Massive APIs — interactive visual explainer | Rudrite Research", "summary": "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.", "body_md": "# Gorilla: Large Language Model Connected with Massive APIs\n\nStop 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.\n\nPatil et al. · arXiv 2023 · Reasoning & RL. [Read the paper ↗](https://arxiv.org/abs/2305.15334)\n\nA 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.\n\n## Questions\n\n- What is Gorilla: Large Language Model Connected with Massive APIs?\n- 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.\n- Who published Gorilla: Large Language Model Connected with Massive APIs, and where?\n- Patil et al. — arXiv 2023 (arXiv:2305.15334).\n- Where can I find a visual explainer of Gorilla: Large Language Model Connected with Massive APIs?\n- Right here — a free, interactive, animated walkthrough of the whole paper, with exhibits computed from the real formulas and verbatim quotes from the source.\n\n## Related explainers\n\n[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)", "url": "https://wpnews.pro/news/gorilla-large-language-model-connected-with-massive-apis-interactive-visual", "canonical_source": "https://research.rudrite.com/gorilla", "published_at": "2026-07-16 00:00:00+00:00", "updated_at": "2026-07-16 13:06:34.920691+00:00", "lang": "en", "topics": ["large-language-models", "artificial-intelligence", "ai-research", "natural-language-processing", "ai-tools"], "entities": ["Gorilla", "LLaMA-7B", "GPT-4", "APIBench", "Patil et al.", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/gorilla-large-language-model-connected-with-massive-apis-interactive-visual", "markdown": "https://wpnews.pro/news/gorilla-large-language-model-connected-with-massive-apis-interactive-visual.md", "text": "https://wpnews.pro/news/gorilla-large-language-model-connected-with-massive-apis-interactive-visual.txt", "jsonld": "https://wpnews.pro/news/gorilla-large-language-model-connected-with-massive-apis-interactive-visual.jsonld"}}