@inproceedings{ouyang-2025-treecut,
title = "{T}ree{C}ut: A Synthetic Unanswerable Math Word Problem Dataset for {LLM} Hallucination Evaluation",
author = "Ouyang, Jialin",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-short.84/",
doi = "10.18653/v1/2025.acl-short.84",
pages = "1073--1085",
ISBN = "979-8-89176-252-7",
abstract = "Large language models (LLMs) now achieve near-human performance on standard math word problem benchmarks (e.g., GSM8K), yet their true reasoning ability remains disputed. A key concern is that models often produce confident, yet unfounded, answers to unanswerable problems. We introduce TreeCut, a synthetic dataset that systematically generates infinite unanswerable math word problems and their answerable counterparts, by representing each question as a tree and removing chosen necessary conditions. Experiments show TreeCut effectively induce hallucinations in large language models, including GPT-4o and o3-mini, with rates of 64{\%} and 44{\%} in their respective worst-case scenarios under zero-shot setting. Further analysis highlights that deeper or more complex trees, composite item names, and removing necessary condition near the middle of a path all increase the likelihood of hallucinations, underscoring the persistent challenges LLMs face in identifying unanswerable math problems. The dataset generation code and sample data are available at \url{https://github.com/j-bagel/treecut-math}."
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%0 Conference Proceedings
%T TreeCut: A Synthetic Unanswerable Math Word Problem Dataset for LLM Hallucination Evaluation
%A Ouyang, Jialin
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-252-7
%F ouyang-2025-treecut
%X Large language models (LLMs) now achieve near-human performance on standard math word problem benchmarks (e.g., GSM8K), yet their true reasoning ability remains disputed. A key concern is that models often produce confident, yet unfounded, answers to unanswerable problems. We introduce TreeCut, a synthetic dataset that systematically generates infinite unanswerable math word problems and their answerable counterparts, by representing each question as a tree and removing chosen necessary conditions. Experiments show TreeCut effectively induce hallucinations in large language models, including GPT-4o and o3-mini, with rates of 64% and 44% in their respective worst-case scenarios under zero-shot setting. Further analysis highlights that deeper or more complex trees, composite item names, and removing necessary condition near the middle of a path all increase the likelihood of hallucinations, underscoring the persistent challenges LLMs face in identifying unanswerable math problems. The dataset generation code and sample data are available at https://github.com/j-bagel/treecut-math.
%R 10.18653/v1/2025.acl-short.84
%U https://aclanthology.org/2025.acl-short.84/
%U https://doi.org/10.18653/v1/2025.acl-short.84
%P 1073-1085
Markdown (Informal)
[TreeCut: A Synthetic Unanswerable Math Word Problem Dataset for LLM Hallucination Evaluation](https://aclanthology.org/2025.acl-short.84/) (Ouyang, ACL 2025)
ACL