Zobrazeno 1 - 10
of 487
pro vyhledávání: '"Xu, Ruifeng"'
Aspect Sentiment Quad Prediction (ASQP) aims to predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review, which is the most representative and challenging task in aspect-based sentiment analysis. A key cha
Externí odkaz:
http://arxiv.org/abs/2406.18078
Autor:
Liu, Ziqiang, Fang, Feiteng, Feng, Xi, Du, Xinrun, Zhang, Chenhao, Wang, Zekun, Bai, Yuelin, Zhao, Qixuan, Fan, Liyang, Gan, Chengguang, Lin, Hongquan, Li, Jiaming, Ni, Yuansheng, Wu, Haihong, Narsupalli, Yaswanth, Zheng, Zhigang, Li, Chengming, Hu, Xiping, Xu, Ruifeng, Chen, Xiaojun, Yang, Min, Liu, Jiaheng, Liu, Ruibo, Huang, Wenhao, Zhang, Ge, Ni, Shiwen
The rapid advancements in the development of multimodal large language models (MLLMs) have consistently led to new breakthroughs on various benchmarks. In response, numerous challenging and comprehensive benchmarks have been proposed to more accurate
Externí odkaz:
http://arxiv.org/abs/2406.05862
Large language models (LLMs) have achieved promising results in sentiment analysis through the in-context learning (ICL) paradigm. However, their ability to distinguish subtle sentiments still remains a challenge. Inspired by the human ability to adj
Externí odkaz:
http://arxiv.org/abs/2406.02911
Numeral systems and units of measurement are two conjoined topics in activities of human beings and have mutual effects with the languages expressing them. Currently, the evaluation of Large Language Models (LLMs) often involves mathematical reasonin
Externí odkaz:
http://arxiv.org/abs/2406.02864
Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training
Publikováno v:
ACL 2024, Main Conference
Large Language Models (LLMs) exhibit substantial capabilities yet encounter challenges, including hallucination, outdated knowledge, and untraceable reasoning processes. Retrieval-augmented generation (RAG) has emerged as a promising solution, integr
Externí odkaz:
http://arxiv.org/abs/2405.20978
Autor:
Fang, Feiteng, Zhu, Liang, Yang, Min, Feng, Xi, Hou, Jinchang, Zhao, Qixuan, Li, Chengming, Hu, Xiping, Xu, Ruifeng
Reinforcement learning from human feedback (RLHF) is a crucial technique in aligning large language models (LLMs) with human preferences, ensuring these LLMs behave in beneficial and comprehensible ways to users. However, a longstanding challenge in
Externí odkaz:
http://arxiv.org/abs/2403.16649
Adverse drug-drug interactions~(DDIs) can compromise the effectiveness of concurrent drug administration, posing a significant challenge in healthcare. As the development of new drugs continues, the potential for unknown adverse effects resulting fro
Externí odkaz:
http://arxiv.org/abs/2403.08377
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