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        <title>Decision-Oriented Dialogue for Human-AI Collaboration</title>
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    <abstract>We describe a class of tasks called decision-oriented dialogues, in which AI assistants such as large language models (LMs) must collaborate with one or more humans via natural language to help them make complex decisions. We formalize three domains in which users face everyday decisions: (1) choosing an assignment of reviewers to conference papers, (2) planning a multi-step itinerary in a city, and (3) negotiating travel plans for a group of friends. In each of these settings, AI assistants and users have disparate abilities that they must combine to arrive at the best decision: Assistants can access and process large amounts of information, while users have preferences and constraints external to the system. For each task, we build a dialogue environment where agents receive a reward based on the quality of the final decision they reach. We evaluate LMs in self-play and in collaboration with humans and find that they fall short compared to human assistants, achieving much lower rewards despite engaging in longer dialogues. We highlight a number of challenges models face in decision-oriented dialogues, ranging from goal-directed behavior to reasoning and optimization, and release our environments as a testbed for future work.</abstract>
    <identifier type="citekey">lin-etal-2024-decision</identifier>
    <identifier type="doi">10.1162/tacl_a_00679</identifier>
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        <date>2024</date>
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            <start>892</start>
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%0 Journal Article
%T Decision-Oriented Dialogue for Human-AI Collaboration
%A Lin, Jessy
%A Tomlin, Nicholas
%A Andreas, Jacob
%A Eisner, Jason
%J Transactions of the Association for Computational Linguistics
%D 2024
%V 12
%I MIT Press
%C Cambridge, MA
%F lin-etal-2024-decision
%X We describe a class of tasks called decision-oriented dialogues, in which AI assistants such as large language models (LMs) must collaborate with one or more humans via natural language to help them make complex decisions. We formalize three domains in which users face everyday decisions: (1) choosing an assignment of reviewers to conference papers, (2) planning a multi-step itinerary in a city, and (3) negotiating travel plans for a group of friends. In each of these settings, AI assistants and users have disparate abilities that they must combine to arrive at the best decision: Assistants can access and process large amounts of information, while users have preferences and constraints external to the system. For each task, we build a dialogue environment where agents receive a reward based on the quality of the final decision they reach. We evaluate LMs in self-play and in collaboration with humans and find that they fall short compared to human assistants, achieving much lower rewards despite engaging in longer dialogues. We highlight a number of challenges models face in decision-oriented dialogues, ranging from goal-directed behavior to reasoning and optimization, and release our environments as a testbed for future work.
%R 10.1162/tacl_a_00679
%U https://aclanthology.org/2024.tacl-1.50/
%U https://doi.org/10.1162/tacl_a_00679
%P 892-911

Markdown (Informal)

[Decision-Oriented Dialogue for Human-AI Collaboration](https://aclanthology.org/2024.tacl-1.50/) (Lin et al., TACL 2024)

• Decision-Oriented Dialogue for Human-AI Collaboration (Lin et al., TACL 2024)

ACL

• Jessy Lin, Nicholas Tomlin, Jacob Andreas, and Jason Eisner. 2024. Decision-Oriented Dialogue for Human-AI Collaboration. Transactions of the Association for Computational Linguistics, 12:892–911.