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This paper studies how humans and large language models linguistically accommodate each other during multi-turn conversations, finding that LLMs overconverge to user style while humans accommodate LLMs no differently than humans.
A practitioner at a company handling ~40k conversations/month describes the bottleneck of manual prompt QA and asks how teams are using automated systems to detect regressions and user frustration in production.
WildFeedback is a novel framework that leverages in-situ user feedback from actual LLM conversations to automatically create preference datasets for aligning language models with human preferences, addressing scalability and bias issues in traditional annotation-based alignment methods.