arXiv AI By Nimol Thuon

KSE-Web: An Analysis of Hybrid Retrieval and LLM-Assisted Query Expansion for Low-Resource Khmer Semantic Search

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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 AI
Sep 4

STAIR (STructure Aware Information Retriever): A novel dataset and LLM based retriever for document structure augmentation

The paper introduces STAIR, a retrieval system that uses a document’s Table of Contents to guide large language models in accessing global structure, thereby reducing hallucinations in Retrieval Augmented Generation. Experiments with a fine‑tuned Differentiable Search Index show that ToC‑based retrieval yields a low hallucination rate (<0.05%) and improves Recall@1 to 82.6% on the newly released SearchTome benchmark, outperforming baselines like BM25, DPR, and Mistral. The authors also release SearchTome, a diverse dataset of 18 books across six domains, to encourage further research in ToC‑based retrieval.

By Vineet Kumar, Meghanadh Pulivarthi, vishwajeet kumar, Jaydeep Sen, Riyaz Ahmad Bhat, Sachindra Joshi
arXiv Computation and Language
Oct 12

Tracing and Taming Hallucinated Terms via Subsequence-Association Filtering and Breadth-Aware Selection for LLM Query Expansion

The paper introduces SAFE‑QE, a training‑free, retriever‑agnostic method for query expansion that mitigates hallucinated terms from large language models. It uses a subsequence‑association filter to score candidate terms based on their reproducibility across randomised query contexts, down‑weighting terms that lack corpus support. SAFE‑QE then selects a broad yet consistent set of expansions via constrained coverage maximisation, and defines a new metric, the Hallucinated Expansion Term Rate, to quantify faithfulness. The approach matches state‑of‑the‑art ranking performance while injecting far fewer unsupported terms on TREC and BEIR benchmarks.

By Amit Kumar Jaiswal, Sagar Uprety, Prayag Tiwari