Papers by Donghun Kang
DoMIX: An Efficient Framework for Exploiting Domain Knowledge in Fine-Tuning (2025.acl-long)
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| Challenge: | Existing methods for domain-adaptive pre-training (DAP) face several limitations: high computational cost and GPU memory usage during training; and lack of generalized model for all end tasks. |
| Approach: | They propose a domain-adaptive pre-training (DAP) method that uses a representative parameter-efficient fine-tuning method to provide pre-trained models for specific tasks. |
| Outcome: | The proposed method can be extended beyond the DAP setting to standard LLM fine-tuning scenarios. |
Verbal-R3: Verbal Reranker as the Missing Bridge between Retrieval and Reasoning (2026.acl-long)
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| Challenge: | Existing paradigms of Retrieval-Augmented Generation (RAG) are suboptimal due to exposure bias, a mismatch between pre-training data distribution and retrieved information. |
| Approach: | They propose to bridge retrieval results and the LLM’s reasoning ability through Verbal Annotations, analytic narratives that explicitly articulate the logical connection between a search query and retrieved contexts. |
| Outcome: | The proposed framework achieves state-of-the-art performance on complex Question Answering benchmarks validating the effectiveness of the proposed framework. |