This article discusses using entropy and variance of entropy (VarEntropy) to detect hallucinations in LLM function calling, focusing on how structured outputs allow for identifying errors through statistical anomalies in token confidence.
The author discusses the development of a function calling large language model (LLM) that significantly improves latency for agentic applications. This LLM matches or even exceeds the performance of other frontier LLMs. It is integrated into an open-source intelligent gateway for agentic applications, allowing developers to focus on more differentiated aspects of their projects. The model and the gateway are available on Hugging Face and GitHub, respectively.
