Zobrazeno 1 - 10
of 308
pro vyhledávání: '"Jin, Yaohui"'
Combining different forms of prompts with pre-trained large language models has yielded remarkable results on reasoning tasks (e.g. Chain-of-Thought prompting). However, along with testing on more complex reasoning, these methods also expose problems
Externí odkaz:
http://arxiv.org/abs/2405.06707
Prompt-based methods have gained increasing attention on NLP and shown validity on many downstream tasks. Many works have focused on mining these methods' potential for knowledge extraction, but few explore their ability to make logical reasoning. In
Externí odkaz:
http://arxiv.org/abs/2405.04872
The next Point of Interest (POI) recommendation aims to recommend the next POI for users at a specific time. As users' check-in records can be viewed as a long sequence, methods based on Recurrent Neural Networks (RNNs) have recently shown good appli
Externí odkaz:
http://arxiv.org/abs/2404.00367
Autor:
Zeng, Kaipeng, yang, Bo, Zhao, Xin, Zhang, Yu, Nie, Fan, Yang, Xiaokang, Jin, Yaohui, Xu, Yanyan
Motivation: Retrosynthesis planning poses a formidable challenge in the organic chemical industry. Single-step retrosynthesis prediction, a crucial step in the planning process, has witnessed a surge in interest in recent years due to advancements in
Externí odkaz:
http://arxiv.org/abs/2404.00044
Large language models (LLMs) still grapple with complex tasks like mathematical reasoning. Despite significant efforts invested in improving prefix prompts or reasoning process, the crucial role of problem context might have been neglected. Accurate
Externí odkaz:
http://arxiv.org/abs/2402.15764
Autor:
Ma, Chang, Zhang, Junlei, Zhu, Zhihao, Yang, Cheng, Yang, Yujiu, Jin, Yaohui, Lan, Zhenzhong, Kong, Lingpeng, He, Junxian
Evaluating large language models (LLMs) as general-purpose agents is essential for understanding their capabilities and facilitating their integration into practical applications. However, the evaluation process presents substantial challenges. A pri
Externí odkaz:
http://arxiv.org/abs/2401.13178
Facility location problems on graphs are ubiquitous in real world and hold significant importance, yet their resolution is often impeded by NP-hardness. Recently, machine learning methods have been proposed to tackle such classical problems, but they
Externí odkaz:
http://arxiv.org/abs/2312.15658
Large language models (LLMs) face challenges in solving complex mathematical problems that require comprehensive capacities to parse the statements, associate domain knowledge, perform compound logical reasoning, and integrate the intermediate ration
Externí odkaz:
http://arxiv.org/abs/2312.08926
In-Context Learning (ICL) is an important paradigm for adapting Large Language Models (LLMs) to downstream tasks through a few demonstrations. Despite the great success of ICL, the limitation of the demonstration number may lead to demonstration bias
Externí odkaz:
http://arxiv.org/abs/2312.07476
Game theory, as an analytical tool, is frequently utilized to analyze human behavior in social science research. With the high alignment between the behavior of Large Language Models (LLMs) and humans, a promising research direction is to employ LLMs
Externí odkaz:
http://arxiv.org/abs/2312.05488