Papers by Kaijie Chen
Understanding and Mitigating Bias Inheritance in LLM-based Data Augmentation on Downstream Tasks (2026.acl-long)
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Miaomiao Li, Hao Chen, Yang Wang, Tingyuan Zhu, Weijia Zhang, Kaijie Zhu, Kam-Fai Wong, Jindong Wang
| Challenge: | Generating synthetic datasets via large language models (LLMs) has emerged as promising approach to improve LLM performance. |
| Approach: | They propose three mitigation strategies to mitigate bias inheritance in LLMs by analyzing real and LLM-augmented data. |
| Outcome: | The proposed methods can work differently on different tasks and biases. |
AgentReview: Exploring Peer Review Dynamics with LLM Agents (2024.emnlp-main)
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| Challenge: | Existing methods of peer review analysis do not address multivariate nature of the process, account for latent variables, and are constrained by privacy concerns due to the sensitive nature of data. |
| Approach: | They propose a large language model based peer review simulation framework which effectively disentangles the impacts of multiple latent factors and addresses the privacy issue. |
| Outcome: | The proposed framework disentangles the impacts of multiple latent factors and addresses privacy concerns. |
R2I-Bench: Benchmarking Reasoning-Driven Text-to-Image Generation (2025.emnlp-main)
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| Challenge: | Reasoning is a fundamental capability underpinning text-to-image (T2I) generation. |
| Approach: | They propose a benchmark to rigorously assess reasoning-driven T2I generation. |
| Outcome: | Experiments with 16 representative T2I models show limited reasoning performance . a strong pipeline-based framework decouples reasoning and generation . |
Shallow Focus, Deep Fixes: Enhancing Shallow Layers Vision Attention Sinks to Alleviate Hallucination in LVLMs (2025.emnlp-main)
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Xiaofeng Zhang, Yihao Quan, Chen Shen, Chaochen Gu, Xiaosong Yuan, Shaotian Yan, Jiawei Cao, Hao Cheng, Kaijie Wu, Jieping Ye
| Challenge: | Multimodal large language models (MLLMs) demonstrate excellent abilities for understanding visual information, but the hallucination remains a challenging problem. |
| Approach: | They propose a training-free approach to enhance vision attention sinks to facilitate convergence of the image token attention sink within shallow layers. |
| Outcome: | The proposed approach improves the convergence of the image token attention sink within shallow layers and strengthens the layer’s focus on the image itself. |