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pro vyhledávání: '"Thai, Katherine"'
Synthetic long-context LLM benchmarks (e.g., "needle-in-the-haystack") test only surface-level retrieval capabilities, but how well can long-context LLMs retrieve, synthesize, and reason over information across book-length inputs? We address this que
Externí odkaz:
http://arxiv.org/abs/2406.16264
With the digital imagery landscape rapidly evolving, image stocks and AI-generated image marketplaces have become central to visual media. Traditional stock images now exist alongside innovative platforms that trade in prompts for AI-generated visual
Externí odkaz:
http://arxiv.org/abs/2404.13784
Exploring Document-Level Literary Machine Translation with Parallel Paragraphs from World Literature
Autor:
Thai, Katherine, Karpinska, Marzena, Krishna, Kalpesh, Ray, Bill, Inghilleri, Moira, Wieting, John, Iyyer, Mohit
Literary translation is a culturally significant task, but it is bottlenecked by the small number of qualified literary translators relative to the many untranslated works published around the world. Machine translation (MT) holds potential to comple
Externí odkaz:
http://arxiv.org/abs/2210.14250
While machine translation evaluation metrics based on string overlap (e.g., BLEU) have their limitations, their computations are transparent: the BLEU score assigned to a particular candidate translation can be traced back to the presence or absence
Externí odkaz:
http://arxiv.org/abs/2210.13746
While numerous architectures for long-range language models (LRLMs) have recently been proposed, a meaningful evaluation of their discourse-level language understanding capabilities has not yet followed. To this end, we introduce ChapterBreak, a chal
Externí odkaz:
http://arxiv.org/abs/2204.10878
Humanities scholars commonly provide evidence for claims that they make about a work of literature (e.g., a novel) in the form of quotations from the work. We collect a large-scale dataset (RELiC) of 78K literary quotations and surrounding critical a
Externí odkaz:
http://arxiv.org/abs/2203.10053
Autor:
Davidov, Nathan, Hernandez, Amanda, Jian, Justin, McKenna, Patrick, Medlin, K. A., Mojumder, Roadra, Owen, Megan, Quijano, Andrew, Rodriguez, Amanda, John, Katherine St., Thai, Katherine, Uraga, Meliza
Tree-based phylogenetic networks, which may be roughly defined as leaf-labeled networks built by adding arcs only between the original tree edges, have elegant properties for modeling evolutionary histories. We answer an open question of Francis, Sem
Externí odkaz:
http://arxiv.org/abs/2009.12413
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