13 citations · 45 across the 24 of their papers we have counts for
14 papers · 1 filter
Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy
Tingjia Shen, Hao Wang, Chuhan Wu +7
Scaling Laws have emerged as a powerful framework for understanding how model performance evolves as they increase in size, providing valuable insights for optimizing computational…
GUI Agents with Foundation Models: A Comprehensive Survey
Shuai Wang, Weiwen Liu, Jingxuan Chen +12
Recent advances in foundation models, particularly Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs), have facilitated the development of intelligent agents…
Beyond Utility: Evaluating LLM as Recommender
Chumeng Jiang, Jiayin Wang, Weizhi Ma +4
With the rapid development of Large Language Models (LLMs), recent studies employed LLMs as recommenders to provide personalized information services for distinct users. Despite ef…
RevisEval: Improving LLM-as-a-Judge via Response-Adapted References
Qiyuan Zhang, Yufei Wang, Tiezheng YU +9
With significant efforts in recent studies, LLM-as-a-Judge has become a cost-effective alternative to human evaluation for assessing text generation quality in a wide range of task…
Performance Law of Large Language Models
Chuhan Wu, Ruiming Tang
Guided by the belief of the scaling law, large language models (LLMs) have achieved impressive performance in recent years. However, scaling law only gives a qualitative estimation…
ToolACE: Winning the Points of LLM Function Calling
Weiwen Liu, Xu Huang, Xingshan Zeng +24
Function calling significantly extends the application boundary of large language models, where high-quality and diverse training data is critical for unlocking this capability. Ho…
All Roads Lead to Rome: Unveiling the Trajectory of Recommender Systems Across the LLM Era
Bo Chen, Xinyi Dai, Huifeng Guo +9
Recommender systems (RS) are vital for managing information overload and delivering personalized content, responding to users' diverse information needs. The emergence of large lan…
Entropy Law: The Story Behind Data Compression and LLM Performance
Mingjia Yin, Chuhan Wu, Yufei Wang +7
Data is the cornerstone of large language models (LLMs), but not all data is useful for model learning. Carefully selected data can better elicit the capabilities of LLMs with much…
Multimodal Pretraining and Generation for Recommendation: A Tutorial
Jieming Zhu, Chuhan Wu, Rui Zhang +1
Personalized recommendation stands as a ubiquitous channel for users to explore information or items aligned with their interests. Nevertheless, prevailing recommendation models pr…
Collaborative-Enhanced Prediction of Spending on Newly Downloaded Mobile Games under Consumption Uncertainty
Peijie Sun, Yifan Wang, Min Zhang +5
With the surge in mobile gaming, accurately predicting user spending on newly downloaded games has become paramount for maximizing revenue. However, the inherently unpredictable na…
LLMTreeRec: Unleashing the Power of Large Language Models for Cold-Start Recommendations
Wenlin Zhang, Chuhan Wu, Xiangyang Li +7
The lack of training data gives rise to the system cold-start problem in recommendation systems, making them struggle to provide effective recommendations. To address this problem,…
Play to Your Strengths: Collaborative Intelligence of Conventional Recommender Models and Large Language Models
Yunjia Xi, Weiwen Liu, Jianghao Lin +5
The rise of large language models (LLMs) has opened new opportunities in Recommender Systems (RSs) by enhancing user behavior modeling and content understanding. However, current a…
Confidence-Aware Multi-Field Model Calibration
Yuang Zhao, Chuhan Wu, Qinglin Jia +6
Accurately predicting the probabilities of user feedback, such as clicks and conversions, is critical for advertisement ranking and bidding. However, there often exist unwanted mis…
Learning to Edit: Aligning LLMs with Knowledge Editing
Yuxin Jiang, Yufei Wang, Chuhan Wu +9
Knowledge editing techniques, aiming to efficiently modify a minor proportion of knowledge in large language models (LLMs) without negatively impacting performance across other inp…