4 citations · 13 across the 22 of their papers we have counts for
22 papers
EnSI-RAG: Entity-Structure-Indexed Retrieval-Augmented Generation for Long-Document Question Answering
Xuanyu Meng, Jiashuo Sun, Jash Rajesh Parekh +1
Question answering (QA) over long, connected documents remains challenging because relevant evidence may span multiple entities and their relationships. Existing retrieval-augmente…
Select-And-Extract: A Lightweight Plugin for Retrieval-Augmented Generation
Chenming Tang, Jiawei Han
Retrieval-augmented generation (RAG) for language model (LM) systems fundamentally has two failure modes: retrieval failure and reading failure. The former fails to recall the righ…
MolE-RAG: Molecular Structure-Enhanced Retrieval-Augmented Generation for Chemistry
Joey Chan, Wonbin Kweon, Ashley Shin +4
Large language models (LLMs) have shown promise for molecular property prediction, but their ability to reason over chemical structures remains limited, as molecular representation…
Harness-1: Reinforcement Learning for Search Agents with State-Externalizing Harnesses
Pengcheng Jiang, Zhiyi Shi, Kelly Hong +5
Search agents are often trained as policies over growing transcripts: the model must decide how to search while also remembering what it has seen, which evidence is useful, which c…
Efficient, Property-Aligned Fan-Out Retrieval via RL-Compiled Diffusion
Pengcheng Jiang, Judith Yue Li, Moonkyung Ryu +8
Many modern retrieval problems are set-valued: given a broad intent, the system must return a collection of results that optimizes higher-order properties (e.g., diversity, coverag…
Learning to Predict Future-Aligned Research Proposals with Language Models
Heng Wang, Pengcheng Jiang, Jiashuo Sun +4
Large language models (LLMs) are increasingly used to assist ideation in research, but evaluating the quality of LLM-generated research proposals remains difficult: novelty and sou…
RareAgent: Self-Evolving Reasoning for Drug Repurposing in Rare Diseases
Lang Qin, Zijian Gan, Xu Cao +5
Computational drug repurposing for rare diseases is especially challenging when no prior associations exist between drugs and target diseases. Therefore, knowledge graph completion…
GRACE: Generative Representation Learning via Contrastive Policy Optimization
Jiashuo Sun, Shixuan Liu, Zhaochen Su +6
Prevailing methods for training Large Language Models (LLMs) as text encoders rely on contrastive losses that treat the model as a black box function, discarding its generative and…
PairSem: LLM-Guided Pairwise Semantic Matching for Scientific Document Retrieval
Wonbin Kweon, Runchu Tian, SeongKu Kang +4
Scientific document retrieval is a critical task for enabling knowledge discovery and supporting research across diverse domains. However, existing dense retrieval methods often st…
Topic Coverage-based Demonstration Retrieval for In-Context Learning
Wonbin Kweon, SeongKu Kang, Runchu Tian +3
The effectiveness of in-context learning relies heavily on selecting demonstrations that provide all the necessary information for a given test input. To achieve this, it is crucia…
Structure-Augmented Reasoning Generation
Jash Rajesh Parekh, Pengcheng Jiang, Jiawei Han
Recent advances in Large Language Models (LLMs) have significantly improved complex reasoning capabilities. Retrieval-Augmented Generation (RAG) has further extended these capabili…
Zero-Shot Open-Schema Entity Structure Discovery
Xueqiang Xu, Jinfeng Xiao, James Barry +7
Entity structure extraction, which aims to extract entities and their associated attribute-value structures from text, is an essential task for text understanding and knowledge gra…
Scientific Paper Retrieval with LLM-Guided Semantic-Based Ranking
Yunyi Zhang, Ruozhen Yang, Siqi Jiao +2
Scientific paper retrieval is essential for supporting literature discovery and research. While dense retrieval methods demonstrate effectiveness in general-purpose tasks, they oft…
s3: You Don't Need That Much Data to Train a Search Agent via RL
Pengcheng Jiang, Xueqiang Xu, Jiacheng Lin +4
Retrieval-augmented generation (RAG) systems empower large language models (LLMs) to access external knowledge during inference. Recent advances have enabled LLMs to act as search…
DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning
Pengcheng Jiang, Jiacheng Lin, Lang Cao +5
Information retrieval systems are crucial for enabling effective access to large document collections. Recent approaches have leveraged Large Language Models (LLMs) to enhance retr…
RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking
Jiaru Zou, Dongqi Fu, Sirui Chen +5
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating them with an external knowledge base to improve the answer relevance and accuracy. In real…
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…
Taxonomy-guided Semantic Indexing for Academic Paper Search
SeongKu Kang, Yunyi Zhang, Pengcheng Jiang +3
Academic paper search is an essential task for efficient literature discovery and scientific advancement. While dense retrieval has advanced various ad-hoc searches, it often strug…
Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval
Pengcheng Jiang, Cao Xiao, Minhao Jiang +4
Large language models (LLMs) have demonstrated significant potential in clinical decision support. Yet LLMs still suffer from hallucinations and lack fine-grained contextual medica…
KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge
Pengcheng Jiang, Lang Cao, Cao Xiao +3
Knowledge Graph Embedding (KGE) techniques are crucial in learning compact representations of entities and relations within a knowledge graph, facilitating efficient reasoning and…
TriSum: Learning Summarization Ability from Large Language Models with Structured Rationale
Pengcheng Jiang, Cao Xiao, Zifeng Wang +3
The advent of large language models (LLMs) has significantly advanced natural language processing tasks like text summarization. However, their large size and computational demands…
GenRES: Rethinking Evaluation for Generative Relation Extraction in the Era of Large Language Models
Pengcheng Jiang, Jiacheng Lin, Zifeng Wang +2
The field of relation extraction (RE) is experiencing a notable shift towards generative relation extraction (GRE), leveraging the capabilities of large language models (LLMs). How…