Zobrazeno 1 - 10
of 180 493
pro vyhledávání: '"typos"'
Autor:
Shimoda, Wataru, Inoue, Naoto, Haraguchi, Daichi, Mitani, Hayato, Uchida, Seichi, Yamaguchi, Kota
While recent text-to-image models can generate photorealistic images from text prompts that reflect detailed instructions, they still face significant challenges in accurately rendering words in the image. In this paper, we propose to retouch erroneo
Externí odkaz:
http://arxiv.org/abs/2411.18159
The robustness of recent Large Language Models (LLMs) has become increasingly crucial as their applicability expands across various domains and real-world applications. Retrieval-Augmented Generation (RAG) is a promising solution for addressing the l
Externí odkaz:
http://arxiv.org/abs/2404.13948
Dense retrieval has become the new paradigm in passage retrieval. Despite its effectiveness on typo-free queries, it is not robust when dealing with queries that contain typos. Current works on improving the typo-robustness of dense retrievers combin
Externí odkaz:
http://arxiv.org/abs/2403.10939
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Textual noise, such as typos or abbreviations, is a well-known issue that penalizes vanilla Transformers for most downstream tasks. We show that this is also the case for sentence similarity, a fundamental task in multiple domains, e.g. matching, ret
Externí odkaz:
http://arxiv.org/abs/2307.02912
Autor:
Zhuang, Shengyao, Shou, Linjun, Pei, Jian, Gong, Ming, Ren, Houxing, Zuccon, Guido, Jiang, Daxin
Current dense retrievers (DRs) are limited in their ability to effectively process misspelled queries, which constitute a significant portion of query traffic in commercial search engines. The main issue is that the pre-trained language model-based e
Externí odkaz:
http://arxiv.org/abs/2304.08138
Autor:
Susik, Robert1 (AUTHOR) rsusik@kis.p.lodz.pl, Grabowski, Szymon1 (AUTHOR)
Publikováno v:
International Journal of Human-Computer Interaction. Sep2024, Vol. 40 Issue 17, p4576-4584. 9p.
Autor:
Zhuang, Shengyao, Zuccon, Guido
Current dense retrievers are not robust to out-of-domain and outlier queries, i.e. their effectiveness on these queries is much poorer than what one would expect. In this paper, we consider a specific instance of such queries: queries that contain ty
Externí odkaz:
http://arxiv.org/abs/2204.00716