Zobrazeno 1 - 10
of 619
pro vyhledávání: '"Meng, Fandong"'
Image generation models have encountered challenges related to scalability and quadratic complexity, primarily due to the reliance on Transformer-based backbones. In this study, we introduce MaskMamba, a novel hybrid model that combines Mamba and Tra
Externí odkaz:
http://arxiv.org/abs/2409.19937
Recently, when dealing with high-resolution images, dominant LMMs usually divide them into multiple local images and one global image, which will lead to a large number of visual tokens. In this work, we introduce AVG-LLaVA, an LMM that can adaptivel
Externí odkaz:
http://arxiv.org/abs/2410.02745
Gender bias has been a focal point in the study of bias in machine translation and language models. Existing machine translation gender bias evaluations are primarily focused on male and female genders, limiting the scope of the evaluation. To assess
Externí odkaz:
http://arxiv.org/abs/2407.16266
As Large Language Models (LLMs) achieve remarkable progress in language understanding and generation, their training efficiency has become a critical concern. Traditionally, LLMs are trained to predict the next token in a sequence. Despite the succes
Externí odkaz:
http://arxiv.org/abs/2407.12665
In-image machine translation (IIMT) aims to translate an image containing texts in source language into an image containing translations in target language. In this regard, conventional cascaded methods suffer from issues such as error propagation, m
Externí odkaz:
http://arxiv.org/abs/2407.02894
Chinese Spell Checking (CSC) aims to detect and correct spelling errors in sentences. Despite Large Language Models (LLMs) exhibit robust capabilities and are widely applied in various tasks, their performance on CSC is often unsatisfactory. We find
Externí odkaz:
http://arxiv.org/abs/2406.16536
Autor:
zhang, Xue, Liang, Yunlong, Meng, Fandong, Zhang, Songming, Chen, Yufeng, Xu, Jinan, Zhou, Jie
Multilingual knowledge editing (MKE) aims to simultaneously revise factual knowledge across multilingual languages within large language models (LLMs). However, most existing MKE methods just adapt existing monolingual editing methods to multilingual
Externí odkaz:
http://arxiv.org/abs/2406.16416
Large language models (LLMs) have exhibited remarkable performance in various natural language processing tasks. Techniques like instruction tuning have effectively enhanced the proficiency of LLMs in the downstream task of machine translation. Howev
Externí odkaz:
http://arxiv.org/abs/2406.08434
Autor:
Cheng, Ning, Guan, Changhao, Gao, Jing, Wang, Weihao, Li, You, Meng, Fandong, Zhou, Jie, Fang, Bin, Xu, Jinan, Han, Wenjuan
Touch holds a pivotal position in enhancing the perceptual and interactive capabilities of both humans and robots. Despite its significance, current tactile research mainly focuses on visual and tactile modalities, overlooking the language domain. In
Externí odkaz:
http://arxiv.org/abs/2406.03813
Recently, Knowledge Editing has received increasing attention, since it could update the specific knowledge from outdated ones in pretrained models without re-training. However, as pointed out by recent studies, existing related methods tend to merel
Externí odkaz:
http://arxiv.org/abs/2406.02882