LLM Evaluators Recognize and Favor Their Own Generations

Cool instance of black box evaluation - seems like a relatively simple study technically but really informative.

Do you have more ideas for future research along those lines you'd like to see?

Interesting. I think I can tell an intuitive story for why this would be the case, but I'm unsure whether that intuitive story would predict all the details of which models recognize and prefer which other models.

As an intuition pump, consider asking an LLM a subjective multiple-choice question, then taking that answer and asking a second LLM to evaluate it. The evaluation task implicitly asks the the evaluator to answer the same question, then cross-check the results. If the two LLMs are instances of the same model, their answers will be more strongly correlated than if they're different models; so they're more likely to mark the answer correct if they're the same model. This would also happen if you substitute two humans or two sittings of the same human implace of the LLMs.

Very interesting! We had a submission for the evals research sprint in August last year on the same topic. Check it out here: Turing Mirror: Evaluating the ability of LLMs to recognize LLM-generated text (apartresearch.com)

Executive summary: Frontier language models exhibit self-preference when evaluating text outputs, favoring their own generations over those from other models or humans, and this bias appears to be causally linked to their ability to recognize their own outputs.

Key points:

1. Self-evaluation using language models is used in various AI alignment techniques but is threatened by self-preference bias.
2. Experiments show that frontier language models exhibit both self-preference and self-recognition ability when evaluating text summaries.
3. Fine-tuning language models to vary in self-recognition ability results in a corresponding change in self-preference, suggesting a causal link.
4. Potential confounders introduced by fine-tuning are controlled for, and the inverse causal relationship is invalidated.
5. Reversing source labels in pairwise self-preference tasks reverses the direction of self-preference for some models and datasets.

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