arXiv AI
Aug 19

DEPT: Document Embedding Preservation Tuning for Unified Query Expansion and Retrieval

The paper introduces DEPT, a method that trains a single decoder-only large language model to both expand queries and encode documents for retrieval. By preserving document embeddings close to their initial cached values while allowing gradients to flow through the generator, DEPT stabilizes retrieval targets and enables efficient index reuse and online hard‑negative mining. Experiments on the BEIR benchmark with Qwen3‑4B‑Instruct‑2507 and LLaMA‑3.2‑3B‑Instruct show that DEPT outperforms training‑free, independently trained, and staged unified baselines, with ablations confirming the benefits of preservation, whitening, end‑to‑end expansion training, and online negatives.

By Jingyuan Wang, Richong Zhang, Zhijie Nie, Mingxin Li, Yanzhao Zhang
arXiv Computation and Language
6d ago

CAGE: Coherence-Aware Graph Encoding for Retrieval-Augmented Generation

CAGE: Coherence-Aware Graph Encoding for Retrieval-Augmented Generation introduces a reranking framework that evaluates and enhances the coherence of retrieved passages across four dimensions—Intra-Domain Relevance, Noise Resistance, Informational Bonding, and Factual Consistency. The method transforms passages into directed heterogeneous entity graphs, reweights factual anchors, encodes structural patterns with a Relational Graph Convolutional Network, and fuses inter-chunk coherence with query relevance to produce a final ranking. Evaluations on four multi‑hop benchmarks show that CAGE matches or surpasses strong baselines, improving Recall@5 on bridge‑dominated datasets and consistently boosting downstream Exact Match scores, indicating that structurally coherent context leads to more precise answers even when retrieval recall is similar or lower.

By Tong Qi, Jingyu Wu, Youbing Yin, Spencer Hong, Daben Liu, Erin Babinsky