Papers by Kejia Chen
Token-level Inference-Time Alignment for Vision-Language Models (2026.findings-acl)
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Kejia Chen, Junjun Zheng, Jiawen Zhang, Manxi Lin, Xiao Pan, Jiacong Hu, Jian Lou, Zunlei Feng, Mingli Song
| Challenge: | Vision-Language Models (VLMs) often prioritize linguistic fluency over visual fidelity . despite widespread adoption, VLMs often exhibit a critical failure mode: hallucination . |
| Approach: | They propose a framework for Token-level Inference-Time Alignment that steers the decoding process without updating the base model parameters. |
| Outcome: | The proposed framework improves performance on 13 benchmarks across architectures . it boosts LLaVA-1.5-7B by 8.6% on MMVet and achieves a 74.0 MMStar score . |
Trident: Self-Supervised Preference Alignment via Triplet Regularization (2026.findings-acl)
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| Challenge: | Large vision-Language Models suffer from noisy supervision and semantic ambiguity in self-supervised settings. |
| Approach: | They propose a self-supervised framework that constructs reliable preference triplets . they propose 'trident' objective that enforces semantic separation between the triplet components . |
| Outcome: | The proposed framework outperforms state-of-the-art RLHF and RLAIF benchmarks on LLaVA-1.5-7B and achieves 95.70% precision on POPE using only 4k self-generated triplets and a single epoch. |
Comparative Graph-based Summarization of Scientific Papers Guided by Comparative Citations (2022.coling-1)
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| Challenge: | Comparative citations can help researchers find related and comparable articles and common topics. |
| Approach: | They propose a comparative graph-based summarization method to find related articles and compare them using citations as guidance. |
| Outcome: | The proposed method outperforms baselines on CSSC and performs well on DUC2006 and DUC2007. |