8 citations · 22 across the 30 of their papers we have counts for
13 papers · 1 filter
Enhancing Table Recognition with Vision LLMs: A Benchmark and Neighbor-Guided Toolchain Reasoner
Yitong Zhou, Mingyue Cheng, Qingyang Mao +2
Pre-trained foundation models have recently made significant progress in table-related tasks such as table understanding and reasoning. However, recognizing the structure and conte…
DisenTS: Disentangled Channel Evolving Pattern Modeling for Multivariate Time Series Forecasting
Zhiding Liu, Jiqian Yang, Qingyang Mao +5
Multivariate time series forecasting plays a crucial role in various real-world applications. Significant efforts have been made to integrate advanced network architectures and tra…
Learning Recommender Systems with Soft Target: A Decoupled Perspective
Hao Zhang, Mingyue Cheng, Qi Liu +3
Learning recommender systems with multi-class optimization objective is a prevalent setting in recommendation. However, as observed user feedback often accounts for a tiny fraction…
Pre-trained Language Model and Knowledge Distillation for Lightweight Sequential Recommendation
Li Li, Mingyue Cheng, Zhiding Liu +3
Sequential recommendation models user interests based on historical behaviors to provide personalized recommendation. Previous sequential recommendation algorithms primarily employ…
ChemEval: A Comprehensive Multi-Level Chemical Evaluation for Large Language Models
Yuqing Huang, Rongyang Zhang, Xuesong He +15
There is a growing interest in the role that LLMs play in chemistry which lead to an increased focus on the development of LLMs benchmarks tailored to chemical domains to assess th…
Revisiting the Solution of Meta KDD Cup 2024: CRAG
Jie Ouyang, Yucong Luo, Mingyue Cheng +4
This paper presents the solution of our team APEX in the Meta KDD CUP 2024: CRAG Comprehensive RAG Benchmark Challenge. The CRAG benchmark addresses the limitations of existing QA…
Bridging User Dynamics: Transforming Sequential Recommendations with Schrödinger Bridge and Diffusion Models
Wenjia Xie, Rui Zhou, Hao Wang +2
Sequential recommendation has attracted increasing attention due to its ability to accurately capture the dynamic changes in user interests. We have noticed that generative models,…
Empowering Few-Shot Relation Extraction with The Integration of Traditional RE Methods and Large Language Models
Ye Liu, Kai Zhang, Aoran Gan +4
Few-Shot Relation Extraction (FSRE), a subtask of Relation Extraction (RE) that utilizes limited training instances, appeals to more researchers in Natural Language Processing (NLP…
Cognitive Evolutionary Learning to Select Feature Interactions for Recommender Systems
Runlong Yu, Qixiang Shao, Qi Liu +2
Feature interaction selection is a fundamental problem in commercial recommender systems. Most approaches equally enumerate all features and interactions by the same pre-defined op…
Towards Personalized Evaluation of Large Language Models with An Anonymous Crowd-Sourcing Platform
Mingyue Cheng, Hao Zhang, Jiqian Yang +7
Large language model evaluation plays a pivotal role in the enhancement of its capacity. Previously, numerous methods for evaluating large language models have been proposed in thi…
A Dataset for the Validation of Truth Inference Algorithms Suitable for Online Deployment
Fei Wang, Haoyu Liu, Haoyang Bi +9
For the purpose of efficient and cost-effective large-scale data labeling, crowdsourcing is increasingly being utilized. To guarantee the quality of data labeling, multiple annotat…
Empowering Sequential Recommendation from Collaborative Signals and Semantic Relatedness
Mingyue Cheng, Hao Zhang, Qi Liu +6
Sequential recommender systems (SRS) could capture dynamic user preferences by modeling historical behaviors ordered in time. Despite effectiveness, focusing only on the \textit{co…
Unlocking the Potential of Large Language Models for Explainable Recommendations
Yucong Luo, Mingyue Cheng, Hao Zhang +3
Generating user-friendly explanations regarding why an item is recommended has become increasingly common, largely due to advances in language generation technology, which can enha…