16 citations · 46 across the 36 of their papers we have counts for
11 papers · 1 filter
Training-free Heterogeneous Graph Condensation via Data Selection
Yuxuan Liang, Wentao Zhang, Xinyi Gao +5
Efficient training of large-scale heterogeneous graphs is of paramount importance in real-world applications. However, existing approaches typically explore simplified models to mi…
Explainable CTR Prediction via LLM Reasoning
Xiaohan Yu, Li Zhang, Chong Chen
Recommendation Systems have become integral to modern user experiences, but lack transparency in their decision-making processes. Existing explainable recommendation methods are hi…
Large Language Model Enhanced Recommender Systems: A Survey
Qidong Liu, Xiangyu Zhao, Yuhao Wang +9
Large Language Model (LLM) has transformative potential in various domains, including recommender systems (RS). There have been a handful of research that focuses on empowering the…
Can LLMs be Good Graph Judge for Knowledge Graph Construction?
Haoyu Huang, Chong Chen, Zeang Sheng +2
In real-world scenarios, most of the data obtained from the information retrieval (IR) system is unstructured. Converting natural language sentences into structured Knowledge Graph…
Mamba Retriever: Utilizing Mamba for Effective and Efficient Dense Retrieval
Hanqi Zhang, Chong Chen, Lang Mei +2
In the information retrieval (IR) area, dense retrieval (DR) models use deep learning techniques to encode queries and passages into embedding space to compute their semantic relat…
KeyVideoLLM: Towards Large-scale Video Keyframe Selection
Hao Liang, Jiapeng Li, Tianyi Bai +7
Recently, with the rise of web videos, managing and understanding large-scale video datasets has become increasingly important. Video Large Language Models (VideoLLMs) have emerged…
SynthVLM: Towards High-Quality and Efficient Synthesis of Image-Caption Datasets for Vision-Language Models
Zheng Liu, Hao Liang, Bozhou Li +5
Vision-Language Models (VLMs) have recently emerged, demonstrating remarkable vision-understanding capabilities. However, training these models requires large-scale datasets, which…
Decoding Matters: Addressing Amplification Bias and Homogeneity Issue for LLM-based Recommendation
Keqin Bao, Jizhi Zhang, Yang Zhang +3
Adapting Large Language Models (LLMs) for recommendation requires careful consideration of the decoding process, given the inherent differences between generating items and natural…
DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment
Haitao Li, Qingyao Ai, Xinyan Han +5
Recent research demonstrates the effectiveness of using pre-trained language models for legal case retrieval. Most of the existing works focus on improving the representation abili…
BLADE: Enhancing Black-box Large Language Models with Small Domain-Specific Models
Haitao Li, Qingyao Ai, Jia Chen +5
Large Language Models (LLMs) like ChatGPT and GPT-4 are versatile and capable of addressing a diverse range of tasks. However, general LLMs, which are developed on open-domain data…
Leveraging Large Language Models for Relevance Judgments in Legal Case Retrieval
Shengjie Ma, Qi Chu, Jiaxin Mao +3
Determining which legal cases are relevant to a given query involves navigating lengthy texts and applying nuanced legal reasoning. Traditionally, this task has demanded significan…