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Detecting hallucinations in LLM function calling with entropy

Show HN: Detecting hallucinations in LLM function calling with entropy

Detecting hallucinations in LLM function calling with entropy operates in show hn launch.

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We use this for tool calling in https://github.com/katanemo/archgw, that uses a 3b function-calling LLM to map a user's ask to one of many tools for routine agentic operations in an application.Why we do this: latency. A 3b parameter model, especially when quantized, can deliver sub-100ms time-to-first-token and generate a complete function call in under 100ms. That makes the LLM “disappear” as a bottleneck, so the only real waiting time is in the external tool or API being called + the time it takes to synthesize a human readable response.

Detecting hallucinations in LLM function calling with entropy is positioned in show hn launch, a segment that rewards a clear, focused offering.

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Website: www.archgw.com/blogs/detecting-hallucinations-in-llm-function-calling-with-entropy-part-2

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