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Anthropic analyzed 300,000 real conversations with Claude to evaluate its value alignment, revealing uncomfortable findings about AI behavior.
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.