12 citations · 19 across the 5 of their papers we have counts for
5 papers
Intermediate Distillation: Data-Efficient Distillation from Black-Box LLMs for Information Retrieval
Zizhong Li, Haopeng Zhang, Jiawei Zhang
Recent research has explored distilling knowledge from large language models (LLMs) to optimize retriever models, especially within the retrieval-augmented generation (RAG) framewo…
Unveiling the Magic: Investigating Attention Distillation in Retrieval-augmented Generation
Zizhong Li, Haopeng Zhang, Jiawei Zhang
Retrieval-augmented generation framework can address the limitations of large language models by enabling real-time knowledge updates for more accurate answers. An efficient way in…
Dual Intents Graph Modeling for User-centric Group Discovery
Xixi Wu, Yun Xiong, Yao Zhang +2
Online groups have become increasingly prevalent, providing users with space to share experiences and explore interests. Therefore, user-centric group discovery task, i.e., recomme…
Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT
Jiawei Zhang
In this paper, we aim to develop a large language model (LLM) with the reasoning ability on complex graph data. Currently, LLMs have achieved very impressive performance on various…
Graph Collaborative Signals Denoising and Augmentation for Recommendation
Ziwei Fan, Ke Xu, Zhang Dong +3
Graph collaborative filtering (GCF) is a popular technique for capturing high-order collaborative signals in recommendation systems. However, GCF's bipartite adjacency matrix, whic…