Papers by Jingwen He
Introducing Graph Context into Language Models through Parameter-Efficient Fine-Tuning for Lexical Relation Mining (2025.acl-long)
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| Challenge: | Pre-trained language models can effectively mine lexical relations between word pairs . however, graph features and semantic knowledge of pre-tried models are lacking in the task. |
| Approach: | They propose a parameter-efficient fine-tuning method which integrates graph features and semantic representations for lexical relation classification and lexic entailment tasks. |
| Outcome: | The proposed method integrates graph features and semantic representations for lexical relation mining tasks. |
Chain-of-Thought Prompting Obscures Hallucination Cues in Large Language Models: An Empirical Evaluation (2025.findings-emnlp)
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| Challenge: | Chain-of-Thought (CoT) prompting can mitigate hallucinations by encouraging step-by-step reasoning, but its impact on halluciation detection remains underexplored. |
| Approach: | They conduct an empirical evaluation of CoT prompting in Large Language Models (LLMs) to examine their impact on hallucination detection methods. |
| Outcome: | The proposed method significantly affects the internal states and token probability distributions of the LLM. |
Fourier Transformer: Fast Long Range Modeling by Removing Sequence Redundancy with FFT Operator (2023.findings-acl)
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| Challenge: | Existing transformer models are computationally demanding and prohibitively costly for long sequences due to the quadratic complexity of its selfattention module. |
| Approach: | They propose a transformer-based model that inherits weights from large pretrained models by removing redundancies in hidden sequences using the ready-made Fast Fourier Transform operator. |
| Outcome: | The proposed model outperforms the standard BART model on the long-range modeling benchmark LRA with significant improvements in speed and space. |
Uni-MMMU: A Massive Multi-discipline Multimodal Unified Benchmark (2026.acl-long)
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Kai Zou, Ziqi Huang, Yuhao Dong, Shulin Tian, Dian Zheng, Hongbo Liu, Jingwen He, Bin Liu, Yu Qiao, Ziwei Liu
| Challenge: | Existing evaluations treat visual understanding and generation in isolation or overlook tasks that inherently couple them. |
| Approach: | They propose a benchmark that examines the bidirectional synergy between generation and understanding across eight reasoning-centric domains. |
| Outcome: | The proposed model systematically unfolds the bidirectional synergy between generation and understanding across eight reasoning-centric domains. |