June 5, 2024, 4:43 a.m. | Christine Herlihy, Jennifer Neville, Tobias Schnabel, Adith Swaminathan

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.01633v1 Announce Type: cross
Abstract: We explore the use of Large Language Model (LLM-based) chatbots to power recommender systems. We observe that the chatbots respond poorly when they encounter under-specified requests (e.g., they make incorrect assumptions, hedge with a long response, or refuse to answer). We conjecture that such miscalibrated response tendencies (i.e., conversational priors) can be attributed to LLM fine-tuning using annotators -- single-turn annotations may not capture multi-turn conversation utility, and the annotators' preferences may not even be …

abstract arxiv assumptions chatbots conjecture conversational cs.ai cs.cl cs.ir cs.lg explore language language model large language large language model llm observe power recommender systems systems type

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