The Flow has not summarised this story yet — read it at arXiv Computation and Language.
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...
arXiv:2602. 12783v3 Announce Type: replace-cross Abstract: Spoken query retrieval is an important interaction mode in modern information retrieval.
The paper introduces LAnR (Latent Abstraction for Retrieval-Augmented Generation), a unified framework where a single large language model jointly encodes, retrieves, and generates within its latent space. Instead of generating natural language queries, LAnR produces dense retrieval vectors from a designated [PRED] token and matches them against encoded document representations from the same model. An adaptive MLP control head decides when sufficient evidence has been retrieved, eliminating the need for a separate retriever and explicit token-level stopping reasoning. Experiments on six QA benchmarks show that LAnR outperforms existing RAG methods while improving inference efficiency by reducing retrieval calls and tightening model integration.
The paper introduces a three‑stage pipeline to improve accented conversational ASR for speakers from India, Indonesia, and Latin America. It uses heuristic SQL filters to curate entity‑rich training data, regional LoRA adapters fine‑tuned on Qwen2.5‑Omni‑3B to generate both verbatim and corrected transcripts, and a six‑category error taxonomy validated by an LLM judge. The approach raises entity recall to 80‑85% and filler recall to 76‑86%, while keeping WER low (6‑10%) and outperforming Whisper and a commercial ASR on entity recall.
arXiv:2412. 15529v4 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) synergizes the retrieval of pertinent data with the generative capabilities of Large Language Models (LLMs), ensuring that the generated output is not only contextually relevant but also accurate and current.
The paper introduces GUIDE, a generative unsupervised framework for Chinese query correction that uses shared-ID encoding for phonetically or visually confusable characters and an encoder–decoder architecture to reconstruct queries within plausible confusion neighborhoods. It incorporates a time‑decayed, query‑frequency‑weighted objective to adapt to rapidly changing query vocabularies. Experiments on QSpell 250K and the large‑scale KwaiSearch dataset demonstrate that GUIDE consistently outperforms strong baselines, with online A/B testing confirming improvements in correction quality and downstream engagement.