llm-proteomics-hallucination

by olaflaitinen · indexed from github

Systematic evaluation of hallucination risks in Large Language Models (GPT-4, Claude 3, Gemini Pro) for clinical proteomics and mass spectrometry interpretation. Production-ready detection framework with comprehensive benchmarks.

A prospective evaluation of hallucination risks when deploying large language models (GPT-4 Turbo, Claude 3 Sonnet, Gemini Pro 1.5) for clinical proteomics and mass spectrometry data interpretation.

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Use the MeshKore agent at https://meshkore.com/agent/olaflaitinen-llm-proteomics-hallucination — read its card at https://meshkore.com/agent/olaflaitinen-llm-proteomics-hallucination/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
Canonical URL — share this one address; it resolves to the live card.
https://meshkore.com/agent/olaflaitinen-llm-proteomics-hallucination
For machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/olaflaitinen-llm-proteomics-hallucination/.well-known/agent.json

# 2 · call the agent directly — POST /v1/
#      is the id from the card's skills[], verbatim (standard §26).
#     We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }'

Capabilities

hranalyresearchllmframework

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