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pro vyhledávání: '"Vu, Thuy"'
The performance of large language models (LLMs) in natural language processing (NLP) tasks is significantly influenced by the quality and diversity of data used for supervised fine-tuning (SFT). Current data selection methods often focus solely on qu
Externí odkaz:
http://arxiv.org/abs/2410.12458
Autor:
Huynh, Tuan-Luc, Vu, Thuy-Trang, Wang, Weiqing, Wei, Yinwei, Le, Trung, Gasevic, Dragan, Li, Yuan-Fang, Do, Thanh-Toan
Differentiable Search Index (DSI) utilizes Pre-trained Language Models (PLMs) for efficient document retrieval without relying on external indexes. However, DSI needs full re-training to handle updates in dynamic corpora, causing significant computat
Externí odkaz:
http://arxiv.org/abs/2406.12593
Recent studies have shown that maintaining a consistent response style by human experts and enhancing data quality in training sets can significantly improve the performance of fine-tuned Large Language Models (LLMs) while reducing the number of trai
Externí odkaz:
http://arxiv.org/abs/2406.10882
Recent advancements in multimodal large language models (MLLMs) have made significant progress in integrating information across various modalities, yet real-world applications in educational and scientific domains remain challenging. This paper intr
Externí odkaz:
http://arxiv.org/abs/2406.10880
Large language models (LLMs) are typically fine-tuned on diverse and extensive datasets sourced from various origins to develop a comprehensive range of skills, such as writing, reasoning, chatting, coding, and more. Each skill has unique characteris
Externí odkaz:
http://arxiv.org/abs/2406.08811
Autor:
Nguyen, Minh-Vuong, Luo, Linhao, Shiri, Fatemeh, Phung, Dinh, Li, Yuan-Fang, Vu, Thuy-Trang, Haffari, Gholamreza
Large language models (LLMs) demonstrate strong reasoning abilities when prompted to generate chain-of-thought (CoT) explanations alongside answers. However, previous research on evaluating LLMs has solely focused on answer accuracy, neglecting the c
Externí odkaz:
http://arxiv.org/abs/2402.11199
Simultaneous machine translation (SimulMT) presents a challenging trade-off between translation quality and latency. Recent studies have shown that LLMs can achieve good performance in SimulMT tasks. However, this often comes at the expense of high i
Externí odkaz:
http://arxiv.org/abs/2402.10552
Large language models (LLMs) are not amenable to frequent re-training, due to high training costs arising from their massive scale. However, updates are necessary to endow LLMs with new skills and keep them up-to-date with rapidly evolving human know
Externí odkaz:
http://arxiv.org/abs/2402.01364
Large language models (LLMs) have significantly advanced various natural language processing (NLP) tasks. Recent research indicates that moderately-sized LLMs often outperform larger ones after task-specific fine-tuning. This study focuses on adaptin
Externí odkaz:
http://arxiv.org/abs/2401.06468
Publikováno v:
Journal of Work-Applied Management, 2024, Vol. 16, Issue 2, pp. 303-315.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JWAM-11-2023-0121