Papers by Tzu-Han Lin
Editing the Mind of Giants: An In-Depth Exploration of Pitfalls of Knowledge Editing in Large Language Models (2024.findings-emnlp)
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| Challenge: | Knowledge editing is a promising technique for updating factual knowledge in large language models (LLMs) but studies have identified side effects such as knowledge distortion and the deterioration of general abilities that have emerged after editing. |
| Approach: | They propose to evaluate the side effects of knowledge editing in large language models using metrics and benchmarks. |
| Outcome: | The results of the study highlight the limitations of current knowledge editing methods and outline potential research directions. |
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. |
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. |