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        <title>Automating Alternative Generation in Decision-Making</title>
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        <namePart type="given">Yevhen</namePart>
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        <namePart type="given">Clara</namePart>
        <namePart type="family">Seyfried</namePart>
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            <title>Findings of the Association for Computational Linguistics: EMNLP 2025</title>
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            <namePart type="given">Christos</namePart>
            <namePart type="family">Christodoulopoulos</namePart>
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        <name type="personal">
            <namePart type="given">Tanmoy</namePart>
            <namePart type="family">Chakraborty</namePart>
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            <namePart type="given">Carolyn</namePart>
            <namePart type="family">Rose</namePart>
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            <namePart type="given">Violet</namePart>
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    <abstract>In decision making, generating alternative solutions is crucial for solving a problem. However, cognitive biases can impede this process by constraining individual decision makers’ creativity. To address this issue, we introduce a new task for automatically generating alternatives, inspired by the process of human “brainstorming”. We define alternative options based on atomic action components and present a dataset of 106 annotated Reddit r/Advice posts containing unique alternative options extracted from users’ replies. We also introduce new metrics to assess the quality of generated components, including distinctiveness, creativity, upvote-weighted, crowd intersection, and final commit intersection scores. As a baseline, we evaluated the large language models (LLMs) LLaMa3:8b, LLaMa3.1:8b, and Gemma 2:9b on the alternative component generation task. On the one hand, models demonstrated high creativity (ability to generate options beyond what Reddit users suggested) and performed well at proposing distinct alternatives. A subset of generated components was manually evaluated and found overall useful. This indicates that LLMs might be used to extend lists of alternative options, helping decision makers consider a problem from different perspectives. On the other hand, LLMs’ outputs often failed to align with human suggestions, implying that they still tend to miss important components.</abstract>
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    <identifier type="doi">10.18653/v1/2025.findings-emnlp.1</identifier>
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%0 Conference Proceedings
%T Automating Alternative Generation in Decision-Making
%A Kostiuk, Yevhen
%A Seyfried, Clara
%A Reed, Chris
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F kostiuk-etal-2025-automating
%X In decision making, generating alternative solutions is crucial for solving a problem. However, cognitive biases can impede this process by constraining individual decision makers’ creativity. To address this issue, we introduce a new task for automatically generating alternatives, inspired by the process of human “brainstorming”. We define alternative options based on atomic action components and present a dataset of 106 annotated Reddit r/Advice posts containing unique alternative options extracted from users’ replies. We also introduce new metrics to assess the quality of generated components, including distinctiveness, creativity, upvote-weighted, crowd intersection, and final commit intersection scores. As a baseline, we evaluated the large language models (LLMs) LLaMa3:8b, LLaMa3.1:8b, and Gemma 2:9b on the alternative component generation task. On the one hand, models demonstrated high creativity (ability to generate options beyond what Reddit users suggested) and performed well at proposing distinct alternatives. A subset of generated components was manually evaluated and found overall useful. This indicates that LLMs might be used to extend lists of alternative options, helping decision makers consider a problem from different perspectives. On the other hand, LLMs’ outputs often failed to align with human suggestions, implying that they still tend to miss important components.
%R 10.18653/v1/2025.findings-emnlp.1
%U https://aclanthology.org/2025.findings-emnlp.1/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.1
%P 1-15

Markdown (Informal)

[Automating Alternative Generation in Decision-Making](https://aclanthology.org/2025.findings-emnlp.1/) (Kostiuk et al., Findings 2025)

• Automating Alternative Generation in Decision-Making (Kostiuk et al., Findings 2025)

ACL

• Yevhen Kostiuk, Clara Seyfried, and Chris Reed. 2025. Automating Alternative Generation in Decision-Making. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 1–15, Suzhou, China. Association for Computational Linguistics.