12 citations · 14 across the 5 of their papers we have counts for
5 papers
PropRAG: Guiding Retrieval with Beam Search over Proposition Paths
Jingjin Wang, Jiawei Han
Retrieval Augmented Generation (RAG) has become the standard approach for equipping Large Language Models (LLMs) with up-to-date knowledge. However, standard RAG, relying on indepe…
Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
Bowen Jin, Hansi Zeng, Zhenrui Yue +5
Efficiently acquiring external knowledge and up-to-date information is essential for effective reasoning and text generation in large language models (LLMs). Prompting advanced LLM…
RAS: Retrieval-And-Structuring for Knowledge-Intensive LLM Generation
Pengcheng Jiang, Lang Cao, Ruike Zhu +5
Large language models (LLMs) have achieved impressive performance on knowledge-intensive tasks, yet they often struggle with multi-step reasoning due to the unstructured nature of…
Chain-of-Factors Paper-Reviewer Matching
Yu Zhang, Yanzhen Shen, SeongKu Kang +3
With the rapid increase in paper submissions to academic conferences, the need for automated and accurate paper-reviewer matching is more critical than ever. Previous efforts in th…
cube2net: Efficient Query-Specific Network Construction with Data Cube Organization
Carl Yang, Mengxiong Liu, Frank He +2
Networks are widely used to model objects with interactions and have enabled various downstream applications. However, in the real world, network mining is often done on particular…