Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Lyu, Yougang"'
Autor:
Lyu, Yougang, Yan, Lingyong, Wang, Zihan, Yin, Dawei, Ren, Pengjie, de Rijke, Maarten, Ren, Zhaochun
As large language models (LLMs) are rapidly advancing and achieving near-human capabilities, aligning them with human values is becoming more urgent. In scenarios where LLMs outperform humans, we face a weak-to-strong alignment problem where we need
Externí odkaz:
http://arxiv.org/abs/2410.07672
Despite large language models (LLMs) increasingly becoming important components of news recommender systems, employing LLMs in such systems introduces new risks, such as the influence of cognitive biases in LLMs. Cognitive biases refer to systematic
Externí odkaz:
http://arxiv.org/abs/2410.02897
Autor:
Zhang, Xiaoyu, Xie, Ruobing, Lyu, Yougang, Xin, Xin, Ren, Pengjie, Liang, Mingfei, Zhang, Bo, Kang, Zhanhui, de Rijke, Maarten, Ren, Zhaochun
Conversational recommender systems (CRSs) are able to elicit user preferences through multi-turn dialogues. They typically incorporate external knowledge and pre-trained language models to capture the dialogue context. Most CRS approaches, trained on
Externí odkaz:
http://arxiv.org/abs/2409.10527
Autor:
Lyu, Yougang, Yan, Lingyong, Wang, Shuaiqiang, Shi, Haibo, Yin, Dawei, Ren, Pengjie, Chen, Zhumin, de Rijke, Maarten, Ren, Zhaochun
Despite their success at many natural language processing (NLP) tasks, large language models still struggle to effectively leverage knowledge for knowledge-intensive tasks, manifesting limitations such as generating incomplete, non-factual, or illogi
Externí odkaz:
http://arxiv.org/abs/2402.11176
Autor:
Lyu, Yougang, Hao, Jitai, Wang, Zihan, Zhao, Kai, Gao, Shen, Ren, Pengjie, Chen, Zhumin, Wang, Fang, Ren, Zhaochun
Multiple defendants in a criminal fact description generally exhibit complex interactions, and cannot be well handled by existing Legal Judgment Prediction (LJP) methods which focus on predicting judgment results (e.g., law articles, charges, and ter
Externí odkaz:
http://arxiv.org/abs/2312.05762
Autor:
Lyu, Yougang, Li, Piji, Yang, Yechang, de Rijke, Maarten, Ren, Pengjie, Zhao, Yukun, Yin, Dawei, Ren, Zhaochun
Natural language understanding (NLU) models often rely on dataset biases rather than intended task-relevant features to achieve high performance on specific datasets. As a result, these models perform poorly on datasets outside the training distribut
Externí odkaz:
http://arxiv.org/abs/2212.05421
Autor:
Lyu, Yougang, Wang, Zihan, Ren, Zhaochun, Ren, Pengjie, Chen, Zhumin, Liu, Xiaozhong, Li, Yujun, Li, Hongsong, Song, Hongye
Publikováno v:
In Information Processing and Management January 2022 59(1)
