Zobrazeno 1 - 10
of 134 138
pro vyhledávání: '"Long-form"'
Autor:
Park, Se Jin, Salazar, Julian, Jansen, Aren, Kinoshita, Keisuke, Ro, Yong Man, Skerry-Ryan, RJ
We consider the generative modeling of speech over multiple minutes, a requirement for long-form multimedia generation and audio-native voice assistants. However, current spoken language models struggle to generate plausible speech past tens of secon
Externí odkaz:
http://arxiv.org/abs/2412.18603
This paper proposes a novel approach to develop an open-domain and long-form Over-The-Top (OTT) Question-Answering (QA) dataset, DragonVerseQA, specifically oriented to the fantasy universe of "House of the Dragon" and "Game Of Thrones" TV series. Mo
Externí odkaz:
http://arxiv.org/abs/2412.16694
Long-form story generation task aims to produce coherent and sufficiently lengthy text, essential for applications such as novel writingand interactive storytelling. However, existing methods, including LLMs, rely on rigid outlines or lack macro-leve
Externí odkaz:
http://arxiv.org/abs/2412.13575
Diffusion models have shown promise in text generation but often struggle with generating long, coherent, and contextually accurate text. Token-level diffusion overlooks word-order dependencies and enforces short output windows, while passage-level d
Externí odkaz:
http://arxiv.org/abs/2412.11333
Despite advances in Large Multi-modal Models, applying them to long and untrimmed video content remains challenging due to limitations in context length and substantial memory overhead. These constraints often lead to significant information loss and
Externí odkaz:
http://arxiv.org/abs/2411.16173
Large language models (LLMs) have demonstrated strong capabilities in text understanding and generation. However, they often lack factuality, producing a mixture of true and false information, especially in long-form generation. In this work, we inve
Externí odkaz:
http://arxiv.org/abs/2411.15993
Autor:
Hosseini, Pedram, Sin, Jessica M., Ren, Bing, Thomas, Bryceton G., Nouri, Elnaz, Farahanchi, Ali, Hassanpour, Saeed
There is a lack of benchmarks for evaluating large language models (LLMs) in long-form medical question answering (QA). Most existing medical QA evaluation benchmarks focus on automatic metrics and multiple-choice questions. While valuable, these ben
Externí odkaz:
http://arxiv.org/abs/2411.09834
Long-form document matching aims to judge the relevance between two documents and has been applied to various scenarios. Most existing works utilize hierarchical or long context models to process documents, which achieve coarse understanding but may
Externí odkaz:
http://arxiv.org/abs/2412.07573
We introduce DAHL, a benchmark dataset and automated evaluation system designed to assess hallucination in long-form text generation, specifically within the biomedical domain. Our benchmark dataset, meticulously curated from biomedical research pape
Externí odkaz:
http://arxiv.org/abs/2411.09255