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        <title>Large Language Models for Predictive Analysis: How Far Are They?</title>
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    <abstract>Predictive analysis is a cornerstone of modern decision-making, with applications in various domains. Large Language Models (LLMs) have emerged as powerful tools in enabling nuanced, knowledge-intensive conversations, thus aiding in complex decision-making tasks. With the burgeoning expectation to harness LLMs for predictive analysis, there is an urgent need to systematically assess their capability in this domain. However, there are no relevant evaluations in existing studies. To bridge this gap, we introduce the PredictiQ benchmark, which integrates 1130 sophisticated predictive analysis queries originating from 44 real-world datasets of 8 diverse fields. We design an evaluation protocol considering text analysis, code generation, and their alignment. Twelve renowned LLMs are evaluated, offering insights into their practical use in predictive analysis.</abstract>
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%0 Conference Proceedings
%T Large Language Models for Predictive Analysis: How Far Are They?
%A Chen, Qin
%A Ren, Yuanyi
%A Ma, Xiaojun
%A Shi, Yuyang
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F chen-etal-2025-large-language
%X Predictive analysis is a cornerstone of modern decision-making, with applications in various domains. Large Language Models (LLMs) have emerged as powerful tools in enabling nuanced, knowledge-intensive conversations, thus aiding in complex decision-making tasks. With the burgeoning expectation to harness LLMs for predictive analysis, there is an urgent need to systematically assess their capability in this domain. However, there are no relevant evaluations in existing studies. To bridge this gap, we introduce the PredictiQ benchmark, which integrates 1130 sophisticated predictive analysis queries originating from 44 real-world datasets of 8 diverse fields. We design an evaluation protocol considering text analysis, code generation, and their alignment. Twelve renowned LLMs are evaluated, offering insights into their practical use in predictive analysis.
%R 10.18653/v1/2025.findings-acl.416
%U https://aclanthology.org/2025.findings-acl.416/
%U https://doi.org/10.18653/v1/2025.findings-acl.416
%P 7961-7978

Markdown (Informal)

[Large Language Models for Predictive Analysis: How Far Are They?](https://aclanthology.org/2025.findings-acl.416/) (Chen et al., Findings 2025)

• Large Language Models for Predictive Analysis: How Far Are They? (Chen et al., Findings 2025)

ACL

• Qin Chen, Yuanyi Ren, Xiaojun Ma, and Yuyang Shi. 2025. Large Language Models for Predictive Analysis: How Far Are They?. In Findings of the Association for Computational Linguistics: ACL 2025, pages 7961–7978, Vienna, Austria. Association for Computational Linguistics.