Papers by Ziyan Li
Grounded Multimodal Named Entity Recognition on Social Media (2023.acl-long)
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| Challenge: | Existing studies on Multimodal Named Entity Recognition only extract entity-type pairs in text, which is useless for multimodal knowledge graph construction. |
| Approach: | They propose a task to identify named entities in text and their bounding box groundings in image . they extend four well-known MNER methods to establish a number of baseline systems . |
| Outcome: | The proposed framework outperforms baseline systems on the GMNER task. |
#HowYouTagTweets: Learning User Hashtagging Preferences via Personalized Topic Attention (2021.emnlp-main)
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| Challenge: | Existing methods based on latent topics cannot capture user interests and thus can't be used to predict how likely a user will post with a hashtag. |
| Approach: | They propose a personalized topic attention model that captures salient contents to personalize hashtag contexts by predicting how likely a user will post with a hashtag. |
| Outcome: | The proposed model significantly outperforms the state-of-the-art recommendation approach without exploiting latent topics. |
From Local to Global: Revisiting Structured Pruning Paradigms for Large Language Models (2026.acl-long)
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Ziyan Wang, Enmao Diao, Qi Le, Pu Wang, Minwoo Lee, Shu-ping Yeh, Evgeny Stupachenko, Hao Feng, Li Yang
| Challenge: | Structured pruning is a practical approach to deploying large language models (LLMs) but it fails to capitalize on modest task-specific calibration signals, causing limited downstream gains. |
| Approach: | They propose a method that removes attention heads and MLP channels using loss-based important scores . they use perplexity for language modeling and a margin-based objective for decision-style tasks . |
| Outcome: | The proposed method lowers perplexity and improves accuracy at higher sparsity . it also stabilizes accuracy and mitigates perxity collapse without fine-tuning . |
FinanceReasoning: Benchmarking Financial Numerical Reasoning More Credible, Comprehensive and Challenging (2025.acl-long)
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Zichen Tang, Haihong E, Ziyan Ma, Haoyang He, Jiacheng Liu, Zhongjun Yang, Zihua Rong, Rongjin Li, Kun Ji, Qing Huang, Xinyang Hu, Yang Liu, Qianhe Zheng
| Challenge: | Compared to existing benchmarks, FinanceReasoning provides three key advancements: (1) credibility; (2) comprehensiveness; (3) numerical precision; (4) complexity; (5) complexity; and (6) complexity. |
| Approach: | They propose a benchmark to evaluate the reasoning capabilities of large reasoning models (LRMs) in financial numerical reasoning problems. |
| Outcome: | The proposed benchmark exceeds existing benchmarks in 67.8% of financial concepts and formulas and is credible, comprehensive, and challenging. |
Self-Correction Makes LLMs Better Parsers (2025.findings-emnlp)
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| Challenge: | Large language models (LLMs) have achieved remarkable success across various natural language processing tasks, but they still face challenges in performing fundamental NLP tasks, such as syntactic parsing. |
| Approach: | They propose a method that leverages grammar rules from existing treebanks to guide LLMs in correcting previous errors. |
| Outcome: | The proposed method significantly improves performance on in-domain and cross-domain datasets. |
Data Augmentation for Cross-domain Parsing via Lightweight LLM Generation and Tree Hybridization (2025.coling-main)
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| Challenge: | Existing approaches for constituency parsing are expensive and lack high-quality labeled data. |
| Approach: | They propose a data augmentation method via lightweight large language model (LLM) generation and tree hybridization to generate a large number of structurally diverse instances. |
| Outcome: | The proposed method achieves significant improvements on five target domains with a lightweight LLM generation cost. |
ReLoop: “Seeing Twice and Thinking Backwards” via Closed-loop Training to Mitigate Hallucinations in Multimodal understanding (2025.findings-emnlp)
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| Challenge: | Existing methods for hallucination mitigation rely on external verification or post-hoc correction, lacking internal mechanism to validate outputs directly during training. |
| Approach: | They propose a unified closed-loop training framework that encourages multimodal consistency for cross-modal understanding in MLLMs. |
| Outcome: | The proposed framework encourages multimodal consistency for cross-modal understanding in MLLMs. |