Zobrazeno 1 - 10
of 601
pro vyhledávání: '"Wong, Derek A"'
LLM self-evaluation relies on the LLM's own ability to estimate response correctness, which can greatly improve its deployment reliability. In this research track, we propose the Chain-of-Embedding (CoE) in the latent space to enable LLMs to perform
Externí odkaz:
http://arxiv.org/abs/2410.13640
Large language models (LLMs) have achieved reasonable quality improvements in machine translation (MT). However, most current research on MT-LLMs still faces significant challenges in maintaining translation consistency and accuracy when processing e
Externí odkaz:
http://arxiv.org/abs/2410.08143
Achieving consistent high-quality machine translation (MT) across diverse domains remains a significant challenge, primarily due to the limited and imbalanced parallel training data available in various domains. While large language models (LLMs) hav
Externí odkaz:
http://arxiv.org/abs/2410.02631
Autor:
Zhou, Zihao, Liu, Shudong, Ning, Maizhen, Liu, Wei, Wang, Jindong, Wong, Derek F., Huang, Xiaowei, Wang, Qiufeng, Huang, Kaizhu
Exceptional mathematical reasoning ability is one of the key features that demonstrate the power of large language models (LLMs). How to comprehensively define and evaluate the mathematical abilities of LLMs, and even reflect the user experience in r
Externí odkaz:
http://arxiv.org/abs/2407.08733
Autor:
Qian, Zhipeng, Zhang, Pei, Yang, Baosong, Fan, Kai, Ma, Yiwei, Wong, Derek F., Sun, Xiaoshuai, Ji, Rongrong
This paper introduces AnyTrans, an all-encompassing framework for the task-Translate AnyText in the Image (TATI), which includes multilingual text translation and text fusion within images. Our framework leverages the strengths of large-scale models,
Externí odkaz:
http://arxiv.org/abs/2406.11432
In recent years, instruction fine-tuning (IFT) on large language models (LLMs) has garnered considerable attention to enhance model performance on unseen tasks. Attempts have been made on automatic construction and effective selection for IFT data. H
Externí odkaz:
http://arxiv.org/abs/2406.07054
Machine translation (MT) has historically faced significant challenges when applied to literary works, particularly in the domain of poetry translation. The advent of Large Language Models such as ChatGPT holds potential for innovation in this field.
Externí odkaz:
http://arxiv.org/abs/2406.03450
Pre-trained Language Models (PLMs) have shown impressive results in various Natural Language Generation (NLG) tasks, such as powering chatbots and generating stories. However, an ethical concern arises due to their potential to produce verbatim copie
Externí odkaz:
http://arxiv.org/abs/2406.00839
Real-world data deviating from the independent and identically distributed (i.i.d.) assumption of in-distribution training data poses security threats to deep networks, thus advancing out-of-distribution (OOD) detection algorithms. Detection methods
Externí odkaz:
http://arxiv.org/abs/2405.14039
The efficacy of an large language model (LLM) generated text detector depends substantially on the availability of sizable training data. White-box zero-shot detectors, which require no such data, are nonetheless limited by the accessibility of the s
Externí odkaz:
http://arxiv.org/abs/2405.04286