Automating Alternative Generation in Decision-Making

TY - GEN

T1 - Automating Alternative Generation in Decision-Making

AU - Kostiuk, Yevhen

AU - Seyfried, Clara

AU - Reed, Chris

N1 - © 2025 Association for Computational Linguistics

PY - 2025/11

Y1 - 2025/11

N2 - 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.

AB - 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.

UR - https://doi.org/10.18653/v1/2025.findings-emnlp

U2 - 10.18653/v1/2025.findings-emnlp.1

DO - 10.18653/v1/2025.findings-emnlp.1

M3 - Conference contribution

SN - 979-8-89176-335-7

SP - 1

EP - 15

BT - Findings of the Association for Computational Linguistics

A2 - Christodoulopoulos, Christos

A2 - Chakraborty, Tanmoy

A2 - Rose, Carolyn

A2 - Peng, Violet

PB - Association for Computational Linguistics

CY - Texas, USA

T2 - The 2025 Conference on Empirical Methods in Natural Language Processing

Y2 - 4 November 2025 through 9 November 2025

ER -