The paper introduces a compact BART-based sequence‑to‑sequence model for low‑latency spell correction of Japanese music search queries, addressing challenges posed by four writing scripts. It employs a script‑aware synthetic misspelling pipeline that blends keyboard‑layout models, phonetic confusion priors, consonant alternations, and kana case errors, and normalizes mixed‑script titles to a single canonical script to reduce hallucinations. Experiments show the model achieves 41.09% exact‑match accuracy and 11.62% CER, outperforming edit‑distance baselines while keeping inference latency under 4 ms on a single GPU.
By Anshul Garg, Pavni Tandon, Karan Bhukar, Tanmay Khandelwal, Ujjal Kumar Dutta
arXiv:2608.22872v1 Announce Type: new
Abstract: Speech-based applications pass spoken queries through automatic speech recognition (ASR) before any retrieval module, so ASR errors enter the pipeline...
By Zhenghua Bao
arXiv:2608.22872v2 Announce Type: new
Abstract: Speech-based applications pass spoken queries through automatic speech recognition (ASR) before any retrieval module, so ASR errors enter the pipeline...
By Zhenghua Bao
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
The study investigates how the remembered content of a user’s query influences retrieval performance for Classical Arabic poetry. By benchmarking nine dense encoders and BM25 across two corpora, three indexing modes, and two evaluation protocols, it finds that BM25 excels on tip‑of‑the‑tongue queries but ranks eighth on abstract queries, while dense encoders outperform BM25 on the latter. The results show that adding remembered half‑verses boosts BM25 dramatically, and that the choice of retrieval protocol can significantly alter perceived system effectiveness.
By Omar Farouk Zouak, Selma Mani, Larbi Said Chikh, Raid Abderrezak Ouahioune
The paper introduces ClariEdge, a dataset of about 3,900 examples that capture ambiguous or ill‑formed user queries in retrieval‑augmented generation contexts, including negative cases that do not need clarification. A lightweight LoRA fine‑tuned model achieves 96.6% classification accuracy, outperforming larger prompted baselines in both classification and clarification generation. The study highlights the importance of hard negatives for distinguishing truly ambiguous queries and demonstrates that the same model can be applied before and after retrieval in a RAG pipeline.
By Lihong He, Lucian Popa, Marina Danilevsky