Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Oyamada, Masafumi"'
Large Language Models (LLMs) have shown remarkable performance improvements and are rapidly gaining adoption in industry. However, the methods for improving LLMs are still designed by humans, which restricts the invention of new model-improving algor
Externí odkaz:
http://arxiv.org/abs/2410.15639
Autor:
Akimoto, Kosuke, Oyamada, Masafumi
In this paper, we address the challenge of optimizing training setups for Large Language Models (LLMs) of low-resource language with a limited amount of corpus. Existing works adopt multi-epoch, multi-lingual, and two-stage training to utilize the li
Externí odkaz:
http://arxiv.org/abs/2410.12325
Open-Domain Multi-Document Summarization (ODMDS) is the task of generating summaries from large document collections in response to user queries. This task is crucial for efficiently addressing diverse information needs from users. Traditional retrie
Externí odkaz:
http://arxiv.org/abs/2406.12494
Retrieval-augmented generation models augment knowledge encoded in a language model by providing additional relevant external knowledge (context) during generation. Although it has been shown that the quantity and quality of context impact the perfor
Externí odkaz:
http://arxiv.org/abs/2403.14197
This paper explores the utilization of LLMs for data preprocessing (DP), a crucial step in the data mining pipeline that transforms raw data into a clean format conducive to easy processing. Whereas the use of LLMs has sparked interest in devising un
Externí odkaz:
http://arxiv.org/abs/2312.01678
Large Language Models (LLMs), typified by OpenAI's GPT, have marked a significant advancement in artificial intelligence. Trained on vast amounts of text data, LLMs are capable of understanding and generating human-like text across a diverse range of
Externí odkaz:
http://arxiv.org/abs/2308.16361
Publikováno v:
VLDB2023
Due to the usefulness in data enrichment for data analysis tasks, joinable table discovery has become an important operation in data lake management. Existing approaches target equi-joins, the most common way of combining tables for creating a unifie
Externí odkaz:
http://arxiv.org/abs/2212.07588
Autor:
Dong, Yuyang, Oyamada, Masafumi
Data scientists are constantly facing the problem of how to improve prediction accuracy with insufficient tabular data. We propose a table enrichment system that enriches a query table by adding external attributes (columns) from data lakes and impro
Externí odkaz:
http://arxiv.org/abs/2204.08235
Finding joinable tables in data lakes is key procedure in many applications such as data integration, data augmentation, data analysis, and data market. Traditional approaches that find equi-joinable tables are unable to deal with misspellings and di
Externí odkaz:
http://arxiv.org/abs/2010.13273
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.