Papers by Hui Qiu
Error Classification of Large Language Models on Math Word Problems: A Dynamically Adaptive Framework (2025.findings-emnlp)
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| Challenge: | Current error classification methods rely on static and predefined categories to capture error patterns. |
| Approach: | They propose a framework for automated dynamic error classification in mathematical reasoning that incorporates common error patterns as explicit guidance. |
| Outcome: | The proposed framework reduces human bias and fine-grained analysis of error patterns. |
A Multi-Agent Framework with Automated Decision Rule Optimization for Cross-Domain Misinformation Detection (2025.emnlp-main)
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| Challenge: | Existing methods for misinformation detection are limited by domain knowledge and expert experience. |
| Approach: | They propose a Multi-Agent Framework for cross-domain misinformation detection with Automated Decision Rule Optimization (MARO) they first employ multiple expert agents to analyze target-domain news, then introduce a question-reflection mechanism that guides expert agents for higher-quality analysis. |
| Outcome: | The proposed framework improves on a common dataset and shows that iteratively improves over existing methods. |
LASA: Language-Agnostic Semantic Alignment at the Semantic Bottleneck for LLM Safety (2026.acl-long)
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Junxiao Yang, Haoran Liu, Jinzhe Tu, Jiale Cheng, Zhexin Zhang, Shiyao Cui, Jiaqi Weng, Jialing Tao, Hui Xue, Hongning Wang, Han Qiu, Minlie Huang
| Challenge: | Large language models (LLMs) have demonstrated better safety performance in high-resource languages than in low-resourced languages. |
| Approach: | They propose language-agnostic semantic alignment (LASA) which anchors safety alignment directly in semantic bottlenecks. |
| Outcome: | The proposed approach significantly improves safety across all languages: average attack success rate drops from 24.7% to 2.8% on LLaMA-3.1-8B-Instruct and remains within 3–4% across Qwen2.5 and Qwend3 Instruct models (7B–32B). |
HEALing Entropy Collapse: Enhancing Exploration in Few-Shot RLVR via Hybrid-Domain Entropy Dynamics Alignment (2026.acl-long)
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| Challenge: | Existing methods for training reasoning-oriented large language models assume high-resource settings with abundant data. |
| Approach: | They propose a framework that integrates high-value general-domain data to promote more diverse exploration. |
| Outcome: | The proposed framework matches or surpasses RLVR trained with 32 target-domain samples using 32 target domain samples. |
Live-Aid: A Large-Scale Dialogue Dataset and Benchmark for Interleaved Multi-party Interactions in Live Streaming (2026.findings-acl)
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Yiming Lei, Yize Fan, Zeming Liu, Jiaji Dong, Hui Qiu, Haitao Leng, Qingjie Liu, Kehai Chen, Tingting Gao, Yunhong Wang
| Challenge: | Existing Multimodal Large Language Models struggle with dynamic interactions due to the scarcity of high-quality interleaved data. |
| Approach: | They propose a large-scale interleaved live interaction Chinese dataset with human-annotated video responses. |
| Outcome: | The proposed model can be used to evaluate live interactions in Chinese over 1,100 hours and 80,037 dialogue turns. |
Benchmarking Hallucination in Large Language Models Based on Unanswerable Math Word Problem (2024.lrec-main)
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| Challenge: | Large language models (LLMs) are highly effective in various natural language processing tasks, but can produce unreliable conjectures in ambiguous contexts, which is known as hallucination. |
| Approach: | They propose a method to evaluate LLM hallucination in Question Answering based on the unanswerable math word problem (UMWP) . they combine text similarity and mathematical expression detection to determine whether LLM considers the question unanswered. |
| Outcome: | The proposed method combines text similarity and mathematical expression detection to determine whether the LLM considers the question unanswerable. |