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\input{uai_2024/macros}

\title{On Overcoming Miscalibrated Conversational Priors in \Copilots}

% Add authors
\author[1]{\href{mailto:<cherlihy@umd.edu>}{Christine Herlihy}{}}
\author[2]{Jennifer Neville}
\author[2]{Tobias Schnabel}
\author[2]{\href{mailto:<adswamin@microsoft.com>}{Adith Swaminathan}{}}
% Add affiliations after the authors
\affil[1]{%
    Department of Computer Science\\
    University of Maryland\\
    College Park, MD USA
}
\affil[2]{%
    Microsoft Research\\
    Redmond, WA USA
}
  
  \begin{document}
\maketitle

\begin{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 representative of users interacting with a recommender system. 
%
We first analyze public LLM chat logs to conclude that query under-specification is common. Next, we study synthetic recommendation problems with configurable latent item utilities, and frame them as Partially Observed Decision Processes (PODP). We find that pre-trained LLMs can be sub-optimal for PODPs and derive better policies that clarify under-specified queries when appropriate. Then, we re-calibrate LLMs by prompting them with learned control messages to approximate the improved policy. Finally, we show empirically that our lightweight learning approach effectively uses logged conversation data to re-calibrate the \actiontypes~of \copilots~for recommendation tasks. 
\end{abstract}

\input{uai_2024/sections/introduction}
\input{uai_2024/sections/problem}
\input{uai_2024/sections/motivating_experiments}
\input{uai_2024/sections/alg_approach}
\input{uai_2024/sections/related_work}
\input{uai_2024/sections/discussion}
\input{uai_2024/sections/conclusion}


\begin{acknowledgements} % will be removed in pdf for initial submission,
						 % (without ‘accepted’ option in \documentclass)
                         % so you can already fill it to test with the
                         % ‘accepted’ class option
We thank Allen Nie, Tara Safavi, Jay Stokes, John Dickerson, Philip Resnik for insightful discussions and the anonymous reviewers of UAI'24 for their feedback. 
\end{acknowledgements}

% References
\bibliography{uai_2024/uai_refs}

\newpage

\onecolumn

\title{On Overcoming Miscalibrated Conversational Priors in \Copilots\\(Supplementary Material)}
\maketitle

\appendix

\input{uai_2024/appendices/helper_llm_classifiers}
\input{uai_2024/appendices/tau_system_messages}
\input{uai_2024/appendices/motivating_experiments}

\end{document}
