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:
Self-evaluation using language models is used in various AI alignment techniques but is threatened by self-preference bias.
Experiments show that frontier language models exhibit both self-preference and self-recognition ability when evaluating text summaries.
Fine-tuning language models to vary in self-recognition ability results in a corresponding change in self-preference, suggesting a causal link.
Potential confounders introduced by fine-tuning are controlled for, and the inverse causal relationship is invalidated.
Reversing source labels in pairwise self-preference tasks reverses the direction of self-preference for some models and datasets.
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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:
Self-evaluation using language models is used in various AI alignment techniques but is threatened by self-preference bias.
Experiments show that frontier language models exhibit both self-preference and self-recognition ability when evaluating text summaries.
Fine-tuning language models to vary in self-recognition ability results in a corresponding change in self-preference, suggesting a causal link.
Potential confounders introduced by fine-tuning are controlled for, and the inverse causal relationship is invalidated.
Reversing source labels in pairwise self-preference tasks reverses the direction of self-preference for some models and datasets.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.