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LLM Evaluators Recognize and Favor Their Own Generations
April 23, 2024, 4:49 a.m. | Arjun Panickssery, Samuel R. Bowman, Shi Feng
cs.CL updates on arXiv.org arxiv.org
Abstract: Self-evaluation using large language models (LLMs) has proven valuable not only in benchmarking but also methods like reward modeling, constitutional AI, and self-refinement. But new biases are introduced due to the same LLM acting as both the evaluator and the evaluatee. One such bias is self-preference, where an LLM evaluator scores its own outputs higher than others' while human annotators consider them of equal quality. But do LLMs actually recognize their own outputs when they …
abstract acting arxiv benchmarking bias biases constitutional ai cs.ai cs.cl evaluation language language models large language large language models llm llms modeling type
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