activity
20222026
most citedDimension Reduction for Efficient Dense Retrieval via Conditional Autoencoder

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL2026

PVFH-Bench: Benchmarking Premise-Induced Visual Fixation Hallucinations in Vision-Language Models

Xinhao Wang, Junbo Cui, Zhenghao Liu +4

Large Vision-Language Models (LVLMs) have achieved remarkable progress across a wide range of multimodal tasks. However, object hallucination remains a persistent challenge, underm…

cs.IR2025

Enhancing the Patent Matching Capability of Large Language Models via the Memory Graph

Qiushi Xiong, Zhipeng Xu, Zhenghao Liu +6

Intellectual Property (IP) management involves strategically protecting and utilizing intellectual assets to enhance organizational innovation, competitiveness, and value creation.…

cs.LG2024

Multi-Evidence based Fact Verification via A Confidential Graph Neural Network

Yuqing Lan, Zhenghao Liu, Yu Gu +4

Fact verification tasks aim to identify the integrity of textual contents according to the truthful corpus. Existing fact verification models usually build a fully connected reason…

cs.IR2023

Text Matching Improves Sequential Recommendation by Reducing Popularity Biases

Zhenghao Liu, Sen Mei, Chenyan Xiong +5

This paper proposes Text mAtching based SequenTial rEcommendation model (TASTE), which maps items and users in an embedding space and recommends items by matching their text repres…

cs.IR2022★ 1 cited

Dimension Reduction for Efficient Dense Retrieval via Conditional Autoencoder

Zhenghao Liu, Han Zhang, Chenyan Xiong +3

Dense retrievers encode queries and documents and map them in an embedding space using pre-trained language models. These embeddings need to be high-dimensional to fit training sig…