Deep Papers Breaking Down EvalGen: Who Validates the Validators?
May 13, 2024
This podcast delves into the complexities of using Large Language Models for evaluation, highlighting the need for human validation in aligning LLM-generated evaluators with user preferences. Topics include developing criteria for acceptable LLM outputs, evaluating email responses, evolving evaluation criteria, template management, LLM validation, and the iterative process of building effective evaluation criteria.
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LLMs Are Powerful Yet Unreliable Judges
- LLMs as judges scale evaluation when ground truth is unavailable and human review is infeasible.
- But their reliability hinges on aligning evaluator prompts with user goals and assumptions.
Three Entry Paths To Build Evaluation Criteria
- EvalGen offers three entry points: inferred criteria, manual criteria, or grading-based bootstrapping.
- The framework treats evaluation as iterative, letting users refine criteria through hands-on grading.
Criteria Should Be Backed By Concrete Assertions
- EvalGen separates 'criteria' from concrete 'assertions' that tell an evaluator exactly what to check.
- Providing multiple ranked assertions improves evaluator consistency and lets users remove poor checks quickly.