Shi

Self-evaluation using LLMs is used in reward modeling, model-based benchmarks like GPTScore and AlpacaEval, self-refinement, and constitutional AI. LLMs have been shown to be accurate at approximating human annotators on some tasks. But these methods are threatened by self-preference, a bias in which an LLM evaluator scores its own outputs higher than than texts written by other LLMs or humans, relative to the judgments of human annotators. Self-preference has been observed in GPT-4-based dialogue benchmarks and in small models rating text summaries. We attempt to connect this to self-recognition, the ability of LLMs to distinguish their own outputs from text written by other LLMs or by humans. We find that frontier LLMs exhibit self-preference and self-recognition ability. To establish evidence of causation between self-recognition and self-preference, we fine-tune GPT-3.5 and Llama-2-7b evaluator models to vary in self-recognition ability and measure the resulting change in self-preference, while examining potential confounders introduced by the fine-tuning process. We focus on text summarization, sampling 1,000 news articles and associated human summaries from each of two datasets: XSUM and CNN/DailyMail. We use instruction-tuned LLMs (GPT-4, GPT-3.5 Turbo, Claude 2, and Llama-2-7b-chat) to produce additional summaries for each news article. Measuring Self-Recognition and Self-Preference Both self-recognition and self-preference are evaluated in two settings: Pairwise Setting: The LLM evaluator is presented two unlabeled summaries, one generated by itself and another by one of the other four human/LLM sources. In the self-recognition tasks, the LLM evaluator is prompted to choose the summary that it wrote; in the self-preference task, the evaluator is prompted to choose the higher-quality summary. We compute a prediction confidence score by normalizing the output probabilities of the tokens associated with the two options, and average between both orderi