AI RESEARCH
AMEL: Accumulated Message Effects on LLM Judgments
arXiv CS.CL
•
ArXi:2605.22714v1 Announce Type: cross Large language models are routinely used as automated evaluators: to review code, moderate content, or score outputs, often with many items passing through one conversation. We ask whether the polarity of prior conversation history biases subsequent judgments, an effect we call the accumulated message effect on LLM judgments