Search Results for author:
Found 15 papers, 7 papers with code
CC-RAG: Structured Multi-Hop Reasoning via Theme-Based Causal Graphs
Understanding cause and effect relationships remains a formidable challenge for Large Language Models (LLMs), particularly in specialized domains where reasoning requires more than surface-level correlations.
Zero-Shot Open-Schema Entity Structure Discovery
These findings suggest that such an enrichment, refinement, and unification mechanism may serve as a principled approach to improving the quality of LLM-based entity structure discovery in various scenarios.
TrialPanorama: Database and Benchmark for Systematic Review and Design of Clinical Trials
This structured and ontology-grounded design enables TrialPanorama to serve as a unified, extensible resource for a wide range of clinical trial tasks, including trial planning, design, and summarization.
s3: You Don't Need That Much Data to Train a Search Agent via RL
Retrieval-augmented generation (RAG) systems empower large language models (LLMs) to access external knowledge during inference.
DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning
We introduce DeepRetrieval, a reinforcement learning (RL) approach that trains LLMs for query generation through trial and error without supervised data (reference query).
RAS: Retrieval-And-Structuring for Knowledge-Intensive LLM Generation
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 retrieved context.
Taxonomy-guided Semantic Indexing for Academic Paper Search
Academic paper search is an essential task for efficient literature discovery and scientific advancement.
Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval
KARE constructs a comprehensive multi-source KG by integrating biomedical databases, clinical literature, and LLM-generated insights, and organizes it using hierarchical graph community detection and summarization for precise and contextually relevant information retrieval.
KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge
Knowledge Graph Embedding (KGE) techniques are crucial in learning compact representations of entities and relations within a knowledge graph, facilitating efficient reasoning and knowledge discovery.
TriSum: Learning Summarization Ability from Large Language Models with Structured Rationale
To overcome this, we introduce TriSum, a framework for distilling LLMs' text summarization abilities into a compact, local model.
GenRES: Rethinking Evaluation for Generative Relation Extraction in the Era of Large Language Models
The field of relation extraction (RE) is experiencing a notable shift towards generative relation extraction (GRE), leveraging the capabilities of large language models (LLMs).
Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations
In this paper, we introduce a novel method called GODE, which accounts for the dual-level structure inherent in molecules.
Text-Augmented Open Knowledge Graph Completion via Pre-Trained Language Models
The mission of open knowledge graph (KG) completion is to draw new findings from known facts.
GraphCare: Enhancing Healthcare Predictions with Personalized Knowledge Graphs
This is because personalized predictions require personalized knowledge graphs (KGs), which are difficult to generate from patient EHR data.
OACAL: Finding Module-consistent Specifications to Secure Systems from Weakened User Obligations
To improve the security with the awareness of unexpected user behaviors, a system can be redesigned to a more robust one by changing the order of actions in its specification.
