paper-conference

MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs
Large language models (LLMs) have shown increasing capability in problem-solving and decision-making, largely based on the step-by-step …
Zhongshen Zeng, Yinhong Liu, Yingjia Wan, Jingyao Li, Pengguang Chen, Jianbo Dai, Yuxuan Yao, Rongwu Xu, Zehan Qi, Wanru Zhao, Linling Shen, Jianqiao Lu, Haochen Tan, Yukang Chen, Hao Zhang, Zhan Shi, Bailin Wang, Zhijiang Guo, Jiaya Jia
Preprint PDF Cite Code Dataset Project 📌NeurIPS'24
MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs
DVD: Dynamic Contrastive Decoding for Knowledge Amplification in Multi-Document Question Answering
Large language models (LLMs) are widely used in question-answering (QA) systems but often generate information with hallucinations. …
Jing Jin, Houfeng Wang, Hao Zhang, Xiaoguang Li, Zhijiang Guo
PDF Cite Code 📌EMNLP'24
DVD: Dynamic Contrastive Decoding for Knowledge Amplification in Multi-Document Question Answering
LONG2RAG: Evaluating Long-Context & Long-Form Retrieval-Augmented Generation with Key Point Recall
Retrieval-augmented generation (RAG) is a promising approach to address the limitations of fixed knowledge in large language models …
Zehan Qi, Rongwu Xu, Zhijiang Guo, Cunxiang Wang, Hao Zhang, Wei Xu
Preprint PDF Cite Code 📌EMNLP'24
LONG2RAG: Evaluating Long-Context & Long-Form Retrieval-Augmented Generation with Key Point Recall
MC-indexing: Effective Long Document Retrieval via Multi-view Content-aware Indexing
Long document question answering (DocQA) aims to answer questions from long documents over 10k words. They usually contain content …
Kuicai Dong, Derrick Goh Xin Deik, Yi Quan Lee, Hao Zhang, Xiangyang Li, Cong Zhang, Yong Liu
Preprint PDF Cite 📌EMNLP'24
MC-indexing: Effective Long Document Retrieval via Multi-view Content-aware Indexing
Collaborative Cross-modal Fusion with Large Language Model for Recommendation
Despite the success of conventional collaborative filtering (CF) approaches for recommendation systems, they exhibit limitations in …
Zhongzhou Liu, Hao Zhang, Kuicai Dong, Yuan Fang
Preprint PDF Cite DOI 📌CIKM'24
Collaborative Cross-modal Fusion with Large Language Model for Recommendation
Retrieval-Oriented Knowledge for Click-Through Rate Prediction
Click-through rate (CTR) prediction plays an important role in personalized recommendations. Recently, sample-level retrieval-based …
Huanshuo Liu, Bo Chen, Menghui Zhu, Jianghao Lin, Jiarui Qin, Yang Yang, Hao Zhang, Ruiming Tang
Preprint PDF Cite Code DOI 📌CIKM'24
Retrieval-Oriented Knowledge for Click-Through Rate Prediction
Planning with Multi-Constraints via Collaborative Language Agents
The rapid advancement of neural language models has sparked a new surge of intelligent agent research. Unlike traditional agents, large …
Cong Zhang, Derrick Goh Xin Deik, Dexun Li, Hao Zhang, Yong Liu
Preprint Cite 📌COLING'25
Planning with Multi-Constraints via Collaborative Language Agents
Parameter-Efficient Conversational Recommender System as a Language Processing Task
Conversational recommender systems (CRS) aim to recommend relevant items to users by eliciting user preference through natural language …
Mathieu Ravaut, Hao Zhang, Lu Xu, Aixin Sun, Yong Liu
Preprint PDF Cite Code Poster Slides 📌EACL'24
Parameter-Efficient Conversational Recommender System as a Language Processing Task
MS-DETR: Natural Language Video Localization with Sampling Moment-Moment Interaction
Given a query, the task of Natural Language Video Localization (NLVL) is to localize a temporal moment in an untrimmed video that …
Jing Wang, Aixin Sun, Hao Zhang, Xiaoli Li
Preprint PDF Cite Code DOI 📌ACL'23
MS-DETR: Natural Language Video Localization with Sampling Moment-Moment Interaction
Interventional Training for Out-Of-Distribution Natural Language Understanding
Out-of-distribution (OOD) settings are used to measure a model’s performance when the distribution of the test data is different …
Sicheng Yu, Jing Jiang, Hao Zhang, Yulei Niu, Qianru Sun, Lidong Bing
PDF Cite Code Poster DOI 📌EMNLP'22
Interventional Training for Out-Of-Distribution Natural Language Understanding