SciText2Eq: Assessing LLMs for Explainable Equation Generation for Scientific Creativity (2026.findings-acl)
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| Challenge: | Prior work has addressed problems in unstructured grounding, multi-equation dependency, and human-aligned evaluation. |
| Approach: | They construct a dataset of scientific texts and evaluate it using an explainable equation generation workflow using automatic metrics and human judgments. |
| Outcome: | The proposed model achieves moderate performance on lexical and syntactic similarity, but struggles with semantic accuracy. |
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| Challenge: | Large Language Models (LLMs) have demonstrated strong performance across various natural language processing tasks, but their proficiency in mathematical reasoning remains a key challenge. |
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ExPerT: Effective and Explainable Evaluation of Personalized Long-Form Text Generation (2025.findings-acl)
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| Challenge: | Evaluating personalized text generated by large language models is challenging, as only the LLM user, i.e. prompt author, can reliably assess the output. |
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On LLM-Based Scientific Inductive Reasoning Beyond Equations (2025.emnlp-main)
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Brian S. Lin, Jiaxin Yuan, Zihan Zhou, Shouli Wang, Shuo Wang, Cunliang Kong, Qi Shi, Yuxuan Li, Liner Yang, Zhiyuan Liu, Maosong Sun
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| Challenge: | introducing Large Language Models (LLMs) has opened new avenues for assessing generated content quality, e.g., coherence, creativity, and context relevance. |
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| Challenge: | Large Language Models (LLMs) and AI assistants are experiencing exponential growth in usage among expert and amateur users. |
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SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis (2025.findings-naacl)
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Hengxing Cai, Xiaochen Cai, Junhan Chang, Sihang Li, Lin Yao, Wang Changxin, Zhifeng Gao, Hongshuai Wang, Li Yongge, Mujie Lin, Shuwen Yang, Jiankun Wang, Mingjun Xu, Jin Huang, Xi Fang, Jiaxi Zhuang, Yuqi Yin, Yaqi Li, Changhong Chen, Zheng Cheng, Zifeng Zhao, Linfeng Zhang, Guolin Ke
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STEM-POM: Evaluating Language Models Math-Symbol Reasoning in Document Parsing (2025.findings-acl)
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| Challenge: | Advances in large language models have spurred research into enhancing their reasoning capabilities, particularly in math-rich STEM documents. |
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MARIO: MAth Reasoning with code Interpreter Output - A Reproducible Pipeline (2024.findings-acl)
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| Challenge: | Large language models lack mathematical reasoning, a hurdle on the path to true artificial general intelligence. |
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