Chen-An Li


2024

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DogeRM: Equipping Reward Models with Domain Knowledge through Model Merging
Tzu-Han Lin | Chen-An Li | Hung-yi Lee | Yun-Nung Chen
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Reinforcement learning from human feedback (RLHF) is a popular strategy for aligning large language models (LLMs) with desired behaviors. Reward modeling is a crucial step in RLHF. However, collecting paired preference data for training reward models is often costly and time-consuming, especially for domain-specific preferences requiring expert annotation. To address this challenge, we propose the **Do**main knowled**ge** merged **R**eward **M**odel (**DogeRM**), a novel framework that integrates domain-specific knowledge into a general reward model by model merging. The experiments demonstrate that DogeRM enhances performance across different benchmarks and provide a detailed analysis showcasing the effects of model merging, showing the great potential of facilitating model alignment.

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Unsupervised Multilingual Dense Retrieval via Generative Pseudo Labeling
Chao-Wei Huang | Chen-An Li | Tsu-Yuan Hsu | Chen-Yu Hsu | Yun-Nung Chen
Findings of the Association for Computational Linguistics: EACL 2024

2023

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Visually-Enhanced Phrase Understanding
Tsu-Yuan Hsu | Chen-An Li | Chao-Wei Huang | Yun-Nung Chen
Findings of the Association for Computational Linguistics: ACL 2023

Large-scale vision-language pre-training has exhibited strong performance in various visual and textual understanding tasks. Recently, the textual encoders of multi-modal pre-trained models have been shown to generate high-quality textual representations, which often outperform models that are purely text-based, such as BERT. In this study, our objective is to utilize both textual and visual encoders of multi-modal pre-trained models to enhance language understanding tasks. We achieve this by generating an image associated with a textual prompt, thus enriching the representation of a phrase for downstream tasks. Results from experiments conducted on four benchmark datasets demonstrate that our proposed method, which leverages visually-enhanced text representations, significantly improves performance in the entity clustering task.

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CONVERSER: Few-shot Conversational Dense Retrieval with Synthetic Data Generation
Chao-Wei Huang | Chen-Yu Hsu | Tsu-Yuan Hsu | Chen-An Li | Yun-Nung Chen
Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue

Conversational search provides a natural interface for information retrieval (IR). Recent approaches have demonstrated promising results in applying dense retrieval to conversational IR. However, training dense retrievers requires large amounts of in-domain paired data. This hinders the development of conversational dense retrievers, as abundant in-domain conversations are expensive to collect. In this paper, we propose Converser, a framework for training conversational dense retrievers with at most 6 examples of in-domain dialogues. Specifically, we utilize the in-context learning capability of large language models to generate conversational queries given a passage in the retrieval corpus. Experimental results on conversational retrieval benchmarks OR-QuAC and TREC CAsT 19 show that the proposed Converser achieves comparable performance to fully-supervised models, demonstrating the effectiveness of our proposed framework in few-shot conversational dense retrieval. All source code and generated datasets are available: https://github.com/MiuLab/CONVERSER