Personas of the Labs: A Tool for Studying LLM Behavioral and Belief Signatures
A practical benchmark for detecting hidden, provider-level alignment signatures in LLMs, the kind that never shows up in standard benchmarks, using relative measurement instead of labeled ground truth.
This talk introduces a practical framework for detecting hidden, provider-level bias (lab signatures) in large language models, the kind that does not show up in standard benchmarks or one-off evaluations. The central idea is that LLM behavior is not just prompt-dependent noise. Models exhibit stable, repeatable tendencies tied to the organization that trained them, for example consistent differences in sycophancy, neutrality, or how they weigh evidence against user intent. This lab-level signal matters most in modern systems where users lock-in to a vendor and many model calls are chained together, such as generation, evaluation, and summarization. The method allows for systematic testing against any dimensions the user cares about for safety and brand use cases.
What you will take away:
- How to detect systematic alignment / belief differences between model providers
- How to measure bias without labeled datasets
- How to contribute to a growing set of alignment benchmarks