Zobrazeno 1 - 10
of 76
pro vyhledávání: '"Yao, Wenlin"'
Autor:
Hu, Yebowen, Wang, Xiaoyang, Yao, Wenlin, Lu, Yiming, Zhang, Daoan, Foroosh, Hassan, Yu, Dong, Liu, Fei
LLMs are ideal for decision-making due to their ability to reason over long contexts and identify critical factors. However, challenges arise when processing transcripts of spoken speech describing complex scenarios. These transcripts often contain u
Externí odkaz:
http://arxiv.org/abs/2410.01772
Autor:
Lin, Fan, Xie, Shuyi, Dai, Yong, Yao, Wenlin, Lang, Tianjiao, Xu, Zishan, Hu, Zhichao, Xiao, Xiao, Liu, Yuhong, Zhang, Yu
As Large Language Models (LLMs) grow increasingly adept at managing complex tasks, the evaluation set must keep pace with these advancements to ensure it remains sufficiently discriminative. Item Discrimination (ID) theory, which is widely used in ed
Externí odkaz:
http://arxiv.org/abs/2409.18892
Despite recent advancements in large language models (LLMs), their performance on complex reasoning problems requiring multi-step thinking and combining various skills is still limited. To address this, we propose a novel framework HDFlow for complex
Externí odkaz:
http://arxiv.org/abs/2409.17433
Autor:
Jing, Liqiang, Huang, Zhehui, Wang, Xiaoyang, Yao, Wenlin, Yu, Wenhao, Ma, Kaixin, Zhang, Hongming, Du, Xinya, Yu, Dong
Large Language Models (LLMs) and Large Vision-Language Models (LVLMs) have demonstrated impressive language/vision reasoning abilities, igniting the recent trend of building agents for targeted applications such as shopping assistants or AI software
Externí odkaz:
http://arxiv.org/abs/2409.07703
Autor:
Hu, Yebowen, Song, Kaiqiang, Cho, Sangwoo, Wang, Xiaoyang, Yao, Wenlin, Foroosh, Hassan, Yu, Dong, Liu, Fei
Reasoning is most powerful when an LLM accurately aggregates relevant information. We examine the critical role of information aggregation in reasoning by requiring the LLM to analyze sports narratives. To succeed at this task, an LLM must infer poin
Externí odkaz:
http://arxiv.org/abs/2406.12084
Autor:
Liang, Zhenwen, Yu, Dian, Yu, Wenhao, Yao, Wenlin, Zhang, Zhihan, Zhang, Xiangliang, Yu, Dong
Large language models (LLMs) have demonstrated impressive capabilities in mathematical problem solving, particularly in single turn question answering formats. However, real world scenarios often involve mathematical question answering that requires
Externí odkaz:
http://arxiv.org/abs/2405.19444
Autor:
Wu, Xuansheng, Zhao, Haiyan, Zhu, Yaochen, Shi, Yucheng, Yang, Fan, Liu, Tianming, Zhai, Xiaoming, Yao, Wenlin, Li, Jundong, Du, Mengnan, Liu, Ninghao
Explainable AI (XAI) refers to techniques that provide human-understandable insights into the workings of AI models. Recently, the focus of XAI is being extended towards Large Language Models (LLMs) which are often criticized for their lack of transp
Externí odkaz:
http://arxiv.org/abs/2403.08946
Autor:
Zhao, Xinran, Zhang, Hongming, Pan, Xiaoman, Yao, Wenlin, Yu, Dong, Wu, Tongshuang, Chen, Jianshu
Publikováno v:
Findings of the Association for Computational Linguistics ACL 2024
For a LLM to be trustworthy, its confidence level should be well-calibrated with its actual performance. While it is now common sense that LLM performances are greatly impacted by prompts, the confidence calibration in prompting LLMs has yet to be th
Externí odkaz:
http://arxiv.org/abs/2402.17124
Autor:
He, Hongliang, Yao, Wenlin, Ma, Kaixin, Yu, Wenhao, Dai, Yong, Zhang, Hongming, Lan, Zhenzhong, Yu, Dong
The rapid advancement of large language models (LLMs) has led to a new era marked by the development of autonomous applications in real-world scenarios, which drives innovation in creating advanced web agents. Existing web agents typically only handl
Externí odkaz:
http://arxiv.org/abs/2401.13919
Autor:
Qin, Yiwei, Song, Kaiqiang, Hu, Yebowen, Yao, Wenlin, Cho, Sangwoo, Wang, Xiaoyang, Wu, Xuansheng, Liu, Fei, Liu, Pengfei, Yu, Dong
This paper introduces the Decomposed Requirements Following Ratio (DRFR), a new metric for evaluating Large Language Models' (LLMs) ability to follow instructions. Addressing a gap in current methodologies, DRFR breaks down complex instructions into
Externí odkaz:
http://arxiv.org/abs/2401.03601