Papers by Chen-An Li
Transferring Textual Preferences to Vision-Language Understanding through Model Merging (2025.acl-short)
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| Challenge: | Large vision-language models (LVLMs) perform outstandingly across multimodal tasks, but training them with preference data is computationally expensive. |
| Approach: | They propose to merge text-based reward models with LVLMs to create visionlanguage reward models (VLRMs) this approach offers an efficient method for incorporating textual preferences into LVRMs. |
| Outcome: | The proposed model improves over LVLMs’ scoring and text-based RMs, and offers an efficient method for incorporating textual preferences into LVRMs. |
Unsupervised Multilingual Dense Retrieval via Generative Pseudo Labeling (2024.findings-eacl)
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| Challenge: | Existing sparse retrieval methods often yield inferior performance in multilingual retrieval, requiring a large amount of paired data, which is costly. |
| Approach: | They propose an Unsupervised Multilingual dense Retriever trained without paired data which iteratively improves performance of multilingual retrievers. |
| Outcome: | The proposed framework outperforms supervised baselines on two benchmark datasets and shows that iterative training improves the performance. |
Visually-Enhanced Phrase Understanding (2023.findings-acl)
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| Challenge: | Large-scale vision-language pre-training models generate high-quality textual representations, which often outperform models that are purely text-based, such as BERT. |
| Approach: | They propose to utilize both textual and visual encoders of multi-modal pre-trained models to enhance language understanding tasks by generating an image associated with a textual prompt. |
| Outcome: | The proposed method outperforms models that are purely text-based on visual and textual understanding tasks and significantly improves the entity clustering task. |
DogeRM: Equipping Reward Models with Domain Knowledge through Model Merging (2024.emnlp-main)
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| Challenge: | Modern large language models (LLMs) showcase impressive capabilities across various tasks with aligning their behavior with human preferences. |
| Approach: | They propose a framework that integrates domain-specific knowledge into a general reward model by model merging. |
| Outcome: | The proposed framework improves performance across different benchmarks and provides detailed analysis showing the effects of model merging. |