Zobrazeno 1 - 10
of 284
pro vyhledávání: '"Wong, Kam-Fai"'
Autor:
Wang, Zezhong, Zeng, Xingshan, Liu, Weiwen, Wang, Yufei, Li, Liangyou, Wang, Yasheng, Shang, Lifeng, Jiang, Xin, Liu, Qun, Wong, Kam-Fai
Current research found the issue of Early Answering in large language models (LLMs), where the models already have an answer before generating the Chain-of-Thought (CoT). This phenomenon suggests a potential lack of necessary dependency between the p
Externí odkaz:
http://arxiv.org/abs/2406.16144
Autor:
Hu, Minda, Zong, Licheng, Wang, Hongru, Zhou, Jingyan, Li, Jingjing, Gao, Yichen, Wong, Kam-Fai, Li, Yu, King, Irwin
Large Language Models (LLMs) have shown great potential in the biomedical domain with the advancement of retrieval-augmented generation (RAG). However, existing retrieval-augmented approaches face challenges in addressing diverse queries and document
Externí odkaz:
http://arxiv.org/abs/2406.11258
Memes, which rapidly disseminate personal opinions and positions across the internet, also pose significant challenges in propagating social bias and prejudice. This study presents a novel approach to detecting harmful memes, particularly within the
Externí odkaz:
http://arxiv.org/abs/2406.09779
Prior study shows that pre-training techniques can boost the performance of visual document understanding (VDU), which typically requires models to gain abilities to perceive and reason both document texts and layouts (e.g., locations of texts and ta
Externí odkaz:
http://arxiv.org/abs/2403.16516
The pervasive spread of misinformation and disinformation in social media underscores the critical importance of detecting media bias. While robust Large Language Models (LLMs) have emerged as foundational tools for bias prediction, concerns about in
Externí odkaz:
http://arxiv.org/abs/2403.14896
In the age of information overload and polarized discourse, understanding media bias has become imperative for informed decision-making and fostering a balanced public discourse. This paper presents IndiTag, an innovative online media bias analysis a
Externí odkaz:
http://arxiv.org/abs/2403.13446
Autor:
Zi, Bojia, Zhao, Shihao, Qi, Xianbiao, Wang, Jianan, Shi, Yukai, Chen, Qianyu, Liang, Bin, Wong, Kam-Fai, Zhang, Lei
Recent advancements in video generation have been remarkable, yet many existing methods struggle with issues of consistency and poor text-video alignment. Moreover, the field lacks effective techniques for text-guided video inpainting, a stark contra
Externí odkaz:
http://arxiv.org/abs/2403.12035
Autor:
Wang, Bingbing, Liang, Bin, Feng, Chun-Mei, Zuo, Wangmeng, Bai, Zhixin, Huang, Shijue, Wong, Kam-Fai, Zeng, Xi, Xu, Ruifeng
In real-world conversations, the diversity and ambiguity of stickers often lead to varied interpretations based on the context, necessitating the requirement for comprehensively understanding stickers and supporting multi-tagging. To address this cha
Externí odkaz:
http://arxiv.org/abs/2403.05428
Autor:
Liang, Bin, Wang, Bingbing, Bai, Zhixin, Lang, Qiwei, Sun, Mingwei, Hou, Kaiheng, Wong, Kam-Fai, Xu, Ruifeng
Using stickers in online chatting is very prevalent on social media platforms, where the stickers used in the conversation can express someone's intention/emotion/attitude in a vivid, tactful, and intuitive way. Existing sticker retrieval research ty
Externí odkaz:
http://arxiv.org/abs/2403.05427
The growing interest in Large Language Models (LLMs) for specialized applications has revealed a significant challenge: when tailored to specific domains, LLMs tend to experience catastrophic forgetting, compromising their general capabilities and le
Externí odkaz:
http://arxiv.org/abs/2403.02756