14 citations · 22 across the 5 of their papers we have counts for
5 papers
Boundary-Aware Multi-Behavior Dynamic Graph Transformer for Sequential Recommendation
Jingsong Su, Xuetao Ma, Mingming Li +2
In the landscape of contemporary recommender systems, user-item interactions are inherently dynamic and sequential, often characterized by various behaviors. Prior research has exp…
SMART: Self-Generating and Self-Validating Multi-Dimensional Assessment for LLMs' Mathematical Problem Solving
Yujie Hou, Mei Wang, Yaoyao Zhong +3
Large Language Models (LLMs) have achieved remarkable performance across a wide range of mathematical benchmarks. However, concerns remain as to whether these successes reflect gen…
ConFit v2: Improving Resume-Job Matching using Hypothetical Resume Embedding and Runner-Up Hard-Negative Mining
Xiao Yu, Ruize Xu, Chengyuan Xue +3
A reliable resume-job matching system helps a company recommend suitable candidates from a pool of resumes and helps a job seeker find relevant jobs from a list of job posts. Howev…
Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning
Hao Chen, Yiming Zhang, Qi Zhang +5
Instruction tuning for large language models (LLMs) has gained attention from researchers due to its ability to unlock the potential of LLMs in following instructions. While instru…
Assessing Hidden Risks of LLMs: An Empirical Study on Robustness, Consistency, and Credibility
Wentao Ye, Mingfeng Ou, Tianyi Li +8
The recent popularity of large language models (LLMs) has brought a significant impact to boundless fields, particularly through their open-ended ecosystem such as the APIs, open-s…