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About this paper
Jisu Kim, Jinhee Park, Changhyun Jeon, Jungwoo Choi, Keonwoo Kim, Minji Hong, Sehyun Kim
A multi-task learning model, developed by researchers at HANCOM, generates structured knowledge like titles, candidate questions, and keywords from document chunks. This method enhances information retrieval effectiveness, achieving a Top@10 accuracy of 95.41% in an optimal configuration, and offers substantial computational efficiency for large-scale systems.
Key insights
- Multi-task learning with a shared encoder significantly improves computational efficiency by enabling parallel generation of diverse metadata from a single encoding operation.
- Generating rich, structured knowledge (titles, candidate questions, keywords) at the chunk level provides more granular and semantically robust signals than traditional document or query expansion.
- Combining generated candidate questions with original document chunks proves highly effective in improving retrieval accuracy, demonstrating the value of auxiliary contextual information.
Summary by alphaXiv.
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