arXiv subjects

Jengnan Tzeng

Publications and source records attributed to Jengnan Tzeng.

2 recordsLinked to original sources

arXiv2025

KAP: MLLM-assisted OCR Text Enhancement for Hybrid Retrieval in Chinese Non-Narrative Documents

Hybrid Retrieval systems, combining Sparse and Dense Retrieval methods, struggle with Traditional Chinese non-narrative documents due to their complex formatting, rich vocabulary, and the insufficient understanding of Chinese synonyms by common embedding models. Previous approaches inadequately address the dual needs of these systems, focusing mainly on general text quality improvement rather than optimizing for retrieval. We propose Knowledge-Aware Preprocessing (KAP), a novel framework that transforms noisy OCR outputs into retrieval-optimized text. KAP adopts a two-stage approach: it first extracts text using OCR, then employs Multimodal Large Language Models to refine the output by integrating visual information from the original documents. This design reduces OCR noise, reconstructs structural elements, and formats the text to satisfy the distinct requirements of sparse and dense retrieval. Empirical results demonstrate that KAP consistently and significantly outperforms conventional preprocessing approaches. Our code is available at https://github.com/JustinHsu1019/KAP.

cs.IR↗

arXiv2025

DAT: Dynamic Alpha Tuning for Hybrid Retrieval in Retrieval-Augmented Generation

Hybrid retrieval techniques in Retrieval-Augmented Generation (RAG) systems enhance information retrieval by combining dense and sparse (e.g., BM25-based) retrieval methods. However, existing approaches struggle with adaptability, as fixed weighting schemes fail to adjust to different queries. To address this, we propose DAT (Dynamic Alpha Tuning), a novel hybrid retrieval framework that dynamically balances dense retrieval and BM25 for each query. DAT leverages a large language model (LLM) to evaluate the effectiveness of the top-1 results from both retrieval methods, assigning an effectiveness score to each. It then calibrates the optimal weighting factor through effectiveness score normalization, ensuring a more adaptive and query-aware weighting between the two approaches. Empirical results show that DAT consistently significantly outperforms fixed-weighting hybrid retrieval methods across various evaluation metrics. Even on smaller models, DAT delivers strong performance, highlighting its efficiency and adaptability.

cs.IR↗