Papers by Yuqi Yang
Revealing COVID-19’s Social Dynamics: Diachronic Semantic Analysis of Vaccine and Symptom Discourse on Twitter (2024.findings-emnlp)
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| Challenge: | Social media data provide a new source for social science and cultural analysis research, but its analysis is challenging due to the semantic shift phenomenon, where word meanings evolve over time. |
| Approach: | They propose an unsupervised dynamic word embedding method to capture longitudinal semantic shifts in social media data without predefined anchor words. |
| Outcome: | The proposed method captures longitudinal semantic shifts in social media data without predefined anchor words and leverages word co-occurrence statistics and dynamic updating to adapt embeddings over time. |
DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories (2024.findings-acl)
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Jia Li, Ge Li, Yunfei Zhao, Yongmin Li, Huanyu Liu, Hao Zhu, Lecheng Wang, Kaibo Liu, Zheng Fang, Lanshen Wang, Jiazheng Ding, Xuanming Zhang, Yuqi Zhu, Yihong Dong, Zhi Jin, Binhua Li, Fei Huang, Yongbin Li, Bin Gu, Mengfei Yang
| Challenge: | Existing benchmarks are poorly aligned with real-world code repositories and are insufficient to evaluate the coding abilities of Large Language Models (LLMs). |
| Approach: | They propose a repository-level benchmark named DevEval to evaluate LLMs' coding abilities in real-world code repositories. |
| Outcome: | The proposed benchmarks show that the LLMs perform better in real-world code repositories than existing benchmarks. |
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
| Challenge: | Existing benchmarks fail to adequately evaluate the proficiency of Large Language Models (LLMs) Existing standards do not cover the skills needed to evaluate LLMs in scientific literature analysis. |
| Approach: | They propose a benchmark to evaluate the proficiency of large language models in scientific literature analysis. |
| Outcome: | SciAssess evaluates 11 LLMs on multiple tasks across scientific fields. |
Exploring Quality and Diversity in Synthetic Data Generation for Argument Mining (2025.emnlp-main)
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| Challenge: | Argument Mining (AM) is hindered by the scarcity of structure-annotated datasets, which are expensive to create manually. |
| Approach: | They propose to use quality-oriented synthesis and diversity-oriented approach to generate argumentative texts with diverse topics and argument structures. |
| Outcome: | The proposed approach significantly improves existing models in full-data and low-resource settings. |
DVMap: Fine-Grained Pluralistic Value Alignment via High-Consensus Demographic-Value Mapping (2026.acl-long)
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| Challenge: | Current Large Language Models (LLMs) rely on coarse-grained national labels for pluralistic value alignment. |
| Approach: | They propose a framework for fine-grained pluralistic value alignment using demographic constraints. |
| Outcome: | The proposed framework can identify groups with predictable, high-consensus value preference . it achieves 48.6% accuracy, surpassing open-source LLM DeepSeek-v3.2 . |
From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan (2026.acl-long)
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Lei Yang, Leiyu Pan, Bojian Xiong, Renren Jin, Shaowei Zhang, Yue Chen, Ling Shi, Jiang Zhou, Junru Wu, Zhen Wang, Jianxiang Peng, Juesi Xiao, Tianyu Dong, Zhuowen Han, Zhuo Chen, Yuqi Ren, Deyi Xiong
| Challenge: | Large language models have achieved remarkable success across a wide range of tasks, yet their performance remains heavily biased toward high-resource languages. |
| Approach: | They propose a pipeline for advancing Tibetan language modeling through multilingual continual pre-training with Tibetan, Chinese, and English. |
| Outcome: | The proposed model outperforms open-source and Tibetan-focused models on diverse tasks. |
Learning First-Order Logic Rules for Argumentation Mining (2025.acl-long)
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Yang Sun, Guanrong Chen, Hamid Alinejad-Rokny, Jianzhu Bao, Yuqi Huang, Bin Liang, Kam-Fai Wong, Min Yang, Ruifeng Xu
| Challenge: | Argumentation Mining (AM) aims to extract argumentative structures from texts by identifying argumentation components (ACs) and their argumentative relations (ARs). |
| Approach: | They propose a First- Order Logic reasoning framework for AM to capture logical reasoning paths within argumentative texts. |
| Outcome: | The proposed framework outperforms strong baselines while significantly improving explainability. |
Vision-Language Introspection: Mitigating Overconfident Hallucinations in MLLMs via Interpretable Bi-Causal Steering (2026.acl-long)
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Shuliang Liu, Songbo Yang, Dong Fang, Sihang Jia, Yuqi Tang, Lingfeng Su, Ruoshui Peng, Yibo Yan, Xin Zou, Xuming Hu
| Challenge: | Existing approaches to overcome object hallucination are limited . Existing mitigations include costly retraining and a training-free inference framework . |
| Approach: | They propose a training-free inference framework that simulates a metacognitive self-correction process. |
| Outcome: | The proposed framework reduces object hallucination rates by 12.67% on MMHal-Bench and improves accuracy by 5.8% on POPE. |
Cognitive Alpha Mining via LLM-Driven Code-Based Evolution (2026.acl-long)
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| Challenge: | Existing approaches to finding effective predictive signals from financial data are limited by their complexity and low signal-to-noise ratio. |
| Approach: | They propose a framework that combines code-level alpha representation with LLM-driven reasoning and evolutionary search. |
| Outcome: | The proposed framework combines code-level alpha representation with LLM-driven reasoning and evolutionary search. |
SessionIntentBench: A Multi-task Inter-session Intention-shift Modeling Benchmark for E-commerce Customer Behavior Understanding (2026.findings-acl)
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Yuqi Yang, Weiqi Wang, Baixuan Xu, Wei Fan, Qing Zong, Chunkit Chan, Zheye Deng, Xin Liu, Yifan Gao, Changlong Yu, Chen Luo, Yang Li, Zheng Li, Qingyu Yin, Bing Yin, Yangqiu Song
| Challenge: | Existing models fail to capture and model customer intention effectively because of insufficient information exploitation and only apparent information like descriptions and titles are used. |
| Approach: | They propose to exploit existing session data to capture and model intention in E-commerce product purchase sessions using a multimodal benchmark. |
| Outcome: | The proposed framework can bridge the gap between intention understanding in simplified research cases like co-buy intention and more complex yet practical scenarios like session history. |