Multi-modal Retrieval-Augmented Generation (RAG) has become a critical method for empowering LLMs by leveraging candidate visual documents. However, current methods consider the entire document as the basic retrieval unit, introducing substantial irrelevant visual content in two ways: 1) Relevant documents often contain large regions unrelated to the query, diluting the focus on salient information; 2) Retrieving multiple documents to increase recall further introduces redundant and irrelevant documents. These redundant contexts distract the model's attention and further degrade the performance. To address this challenge, we propose RegionRAG, a novel framework that shifts the retrieval paradigm from the document level to the region level. During training, we design a hybrid supervision strategy from both labeled data and unlabeled data to pinpoint relevant patches. During inference, we propose a dynamic pipeline that intelligently groups salient patches into complete semantic regions. By delegating the task of identifying relevant regions to the retriever, RegionRAG enables the generator to focus solely on concise, query-relevant visual content, improving both efficiency and accuracy. Experiments on six benchmarks demonstrate that RegionRAG achieves state-of-the-art performance. It improves retrieval accuracy by 10.02% in R@1 on average, and boosts question answering accuracy by 3.56% while using only 71.42% visual tokens compared with prior methods.

Visual captioning benchmarks have become outdated with the emergence of modern multimodal large language models (MLLMs), as the brief ground-truth sentences and traditional metrics fail to assess detailed captions effectively. While recent benchmarks attempt to address this by focusing on keyword extraction or object-centric evaluation, they remain limited to vague-view or object-view analyses and incomplete visual element coverage. In this paper, we introduce CAPability, a comprehensive multi-view benchmark for evaluating visual captioning across 12 dimensions spanning six critical views. We curate nearly 11K human-annotated images and videos with visual element annotations to evaluate the generated captions. CAPability stably assesses both the correctness and thoroughness of captions with \textit{precision} and \textit{hit} metrics. By converting annotations to QA pairs, we further introduce a heuristic metric, \textit{know but cannot tell} ($K\bar{T}$), indicating a significant performance gap between QA and caption capabilities. Our work provides a holistic analysis of MLLMs' captioning abilities, as we identify their strengths and weaknesses across various dimensions, guiding future research to enhance specific aspects of their capabilities.

End-to-end automatic speech recognition (ASR) systems often struggle to recognize rare name entities, such as personal names, organizations and terminologies that are not frequently encountered in the training data. This paper presents Contextual Biasing Whisper (CB-Whisper), a novel ASR system based on OpenAI’s Whisper model that can recognize user-defined name entities by performing open-vocabulary keyword-spotting (KWS) before the decoder. The KWS module leverages text-to-speech (TTS) techniques and a convolutional neural network (CNN) classifier to match the features between the entities and the utterances. To integrate the recognized entities into the Whipser decoder and avoid hallucinations, we carefully crafted multiple prompts with spoken form hints. Experiments show that the KWS module based on Whisper encoder’s features can recognize unseen user-defined keywords effectively. More importantly, the proposed CB-Whisper substantially improves the mixed-error-rate (MER) and entity recall compared to the original Whisper model on three internal datasets and two publicly available datasets including Aishell and ACL datasets that cover English-only, Chinese-only, and code-switching scenarios.

The recognition of rare named entities, such as personal names and terminologies, is challenging for automatic speech recognition (ASR) systems, especially when they are not frequently observed in the training data.In this paper, we introduce keyword spotting enhanced Whisper (KWS-Whisper), a novel ASR system that leverages the Whisper model and performs openvocabulary keyword spotting (OV-KWS) on the hidden states of the Whisper encoder to recognize user-defined named entities.These entities serve as prompts for the Whisper decoder.To optimize the model, we propose a multitask training approach that learns OV-KWS and contextual-ASR tasks.We evaluate our approach on Chinese Aishell hot word subsets and two internal code-switching test sets and show that it significantly improves the entity recall compared to the original Whisper model.Moreover, we demonstrate that the OV-KWS can be a plug-andplay module to enhance the ASR error correction methods and frozen Whisper models.

Entity correction is crucial in Automatic Speech TABLE I Recognition (ASR), since erroneous entities seriously affect our understanding of ASR results. In this paper, in order to correct entity errors, we propose a knowledge prompt approach for Whisper (a recent ASR model trained with a corpus containing 680k hours of labeled speech recorded in various conditions). For a given audio, our approach consists of three steps: (1) obtaining its ASR result by Whisper; (2) fuzzy matching the ASR result with a knowledge base to obtain candidate entities; (3) using the candidate entities as a prompt to obtain the final ASR result by Whisper again. We conduct experiments on the test dataset of open-source Chinese speech corpus AISHELLNER. Experimental results show that our approach not only significantly improves the entity recall rate in ASR results (from 70.97% to 84.82%), but also reduces the overall Character Error Rate (CER).

Error correction techniques have been used to refine the output sentences from automatic speech recognition (ASR) models and achieve a lower word error rate (WER). Previous works usually adopt end-to-end models and has strong dependency on Pseudo Paired Data and Original Paired Data. But when only pre-training on Pseudo Paired Data, previous models have negative effect on correction. While fine-tuning on Original Paired Data, the source side data must be transcribed by a well-trained ASR model, which takes a lot of time and not universal. In this paper, we propose UCorrect, an unsupervised Detector-Generator-Selector framework for ASR Error Correction. UCorrect has no dependency on the training data mentioned before. The whole procedure is first to detect whether the character is erroneous, then to generate some candidate characters and finally to select the most confident one to replace the error character. Experiments on the public AISHELL-1 dataset and WenetSpeech dataset show the effectiveness of UCorrect for ASR error correction: 1) it achieves significant WER reduction, achieves 6.83% even without fine-tuning and 14.29% after fine-tuning; 2) it outperforms the popular NAR correction models by a large margin with a competitive low latency; and 3) it is an universal method, as it reduces all WERs of the ASR model with different decoding strategies and reduces all WERs of ASR models trained on different scale datasets.

Punctuation restoration can be crucial for the cascade speech translation system. Traditional approaches typically treat it as a sequential tagging problem, predicting which punctuation mark should follow a given word. However, this often requires significant computational and storage resources for full-stage training or fine-tuning. Our argument is that pre-trained language models (PLMs) can directly leverage their learned knowledge for punctuation generation, making additional training unnecessary. In this paper, we propose the Zephyr algorithm, which utilizes PLMs to perform zero-shot and few-shot punctuation restoration for both offline and streaming scenarios. Our experimental results demonstrate that, in comparison to fine-tuning-based baselines, Zephyr achieves competitive performance while requiring little to no training cost and exhibiting better generalizability in zeroshot and few-shot settings.

Autoregressive (AR) and Non-autoregressive (NAR) models have their own superiority on the performance and latency, combining them into one model may take advantage of both. Current combination frameworks focus more on the integration of multiple decoding paradigms with a unified generative model, e.g. Masked Language Model. However, the generalization can be harmful on the performance due to the gap between training objective and inference. In this paper, we aim to close the gap by preserving the original objective of AR and NAR under a unified framework. Specifically, we propose the Directional Transformer (Diformer) by jointly modelling AR and NAR into three generation directions (left-to-right, right-to-left and straight) with a newly introduced direction variable, which works by controlling the prediction of each token to have specific dependencies under that direction. The unification achieved by direction successfully preserves the original dependency assumption used in AR and NAR, retaining both generalization and performance. Experiments on 4 WMT benchmarks demonstrate that Diformer outperforms current united-modelling works with more than 1.5 BLEU points for both AR and NAR decoding, and is also competitive to the state-of-the-art independent AR and NAR models.