Papers by Xiaorui Li
Automated Creativity Evaluation for Large Language Models: A Reference-Based Approach (2025.findings-emnlp)
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| Challenge: | Existing methods for evaluating creativity of machine-generated texts rely on costly manual annotations or fail to align closely with human assessments. |
| Approach: | They propose an automated method based on the Torrance Test of Creative Writing (TTCW) . |
| Outcome: | The proposed method improves the alignment between LLM evaluations and human assessments. |
LaTeX2Solver: a Hierarchical Semantic Parsing of LaTeX Document into Code for an Assistive Optimization Modeling Application (2023.acl-demo)
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Rindra Ramamonjison, Timothy Yu, Linzi Xing, Mahdi Mostajabdaveh, Xiaorui Li, Xiaojin Fu, Xiongwei Han, Yuanzhe Chen, Ren Li, Kun Mao, Yong Zhang
| Challenge: | Existing systems that translate optimization formulas manually are cumbersome and time-consuming. |
| Approach: | They propose a system that converts optimization formulas from TeX document to solver language. |
| Outcome: | The proposed system helps operations research practitioners convert optimization formulations into solver modeling languages. |
TRIDENT: Enhancing Large Language Model Safety with Tri-Dimensional Diversified Red-Teaming Data Synthesis (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) excel in natural language processing tasks but are vulnerable to harmful content and being exploited for malicious purposes. |
| Approach: | They propose a framework to measure the risk coverage of alignment datasets across three dimensions: Lexical Diversity, Malicious Intent, and Jailbreak Tactics. |
| Outcome: | The proposed framework measures risk coverage across Lexical Diversity, Malicious Intent, and Jailbreak Tactics. |
Fin-STAR: Structure-as-Semantics to Resolve Implicitness in Financial Retrieval (2026.findings-acl)
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Yu Zou, Yan Chen, Lida He, Qi Zhou, Xiaorui Zhou, Aixi Zhong, Yi Wang, Wei Li, Qingyu Wang, Jiatao Li, Wei Gong, Jialei Zeng, Jingmei Zhao, Ke Jiang, Qing Li
| Challenge: | Existing Retrieval-Augmented Generation systems treat structure as a physical navigational skeleton rather than intrinsic semantic knowledge. |
| Approach: | They propose a framework that redefining hierarchy as intrinsic semantics and uses snippets to enrich hierarchical lineage. |
| Outcome: | The proposed framework outperforms state-of-the-art hierarchical and graph-based benchmarks on FinTierQA Gold. |