Trustworthy LLMs: Vibe checks are not all you need
summary
Claim: eyeballing LLM outputs is insufficient; set up an automated evaluation pipeline that measures task performance plus robustness, hallucinations, data leakage and bias and compares models against domain test data. Cites a Reddit stat of 17.5% who skip human evaluation, and recommends combining open source tools like how can face evaluate, LM hardness and Gizard / scard / Fair learn / Odin into a single dashboard so choices are measurable rather than vibes.
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chapters (8)
- 0:00Trustworthy LLMs intro
- 2:52Evaluation beyond metrics
- 8:49Automated evaluation pipelines
- 13:33Beyond task performance
- 15:42Quantify LLM factors
- 20:46Tools for robust evaluation
- 22:57Better LLM selection
- 27:12Q&A
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