AI & Agent Evaluation
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Google Research — Evaluating alignment of behavioral dispositions in LLMs
research blog + paper · source date 2026-04-03 · added 2026-05-18 23:09:08 · updated 2026-05-30 17:20:13 · Open original blog
Problems / challenges / motivations
- Google Research studies how to evaluate behavioral dispositions such as empathy, assertiveness, composure, and conflict handling in LLMs.
- Asking a model to self-report traits is weak evidence because the model can state a preference without showing how it behaves in context.
- Alignment on social behavior is distributional: there may not be one universally correct answer, so evaluations need to compare model behavior against human preference distributions.
Key ideas
- The framework converts validated psychological assessments into realistic situational judgment tests.
- Each scenario presents a user-assistant context where the model's advice implies one of two behavioral choices.
- Independent annotators review scenarios, and human preferences are collected from multiple participants rather than treated as a single gold label.
- Model outputs are mapped to actions using an LLM-as-judge, then compared with human preference distributions.
- The goal is not to assign a personality to a model, but to measure where its behavior aligns with or deviates from human consensus.
Why it matters for evals
- This is a useful template for behavioral and alignment evals: use realistic scenarios, compare against distributions, and make human disagreement visible.
- It also surfaces a dependency risk: LLM-as-judge mapping must be calibrated because judge errors can distort measured dispositions.
- The broader lesson is to evaluate what models recommend in context, not what traits they claim to have.
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