Frontiers

Large Language Models for Knowledge-Intensive Domains: Methods, Systems, and Evaluation

The rapid emergence of Large Language Models (LLMs) and generative AI is reshaping how people and organizations access, synthesize, and apply knowledge.
IEEE

Toward the Extension and Enhanced Representation for Ambiguous Query With Search Heterogeneous Graph Learning

Abstract: In an online search, users often input an ambiguous short query to search engines, which leads to search engines being unable to accurately understand the true users’ query intent. Thus, ...
IEEE

QUERY2BERT: Combining Knowledge Graph and Language Model for Reasoning on Logical Queries

Abstract: Answering logical questions with a knowledge graph has been a critical research focus because this needs to reason and synthesize information. Previous studies have mainly dealt with logical ...
GitHub

CGBridge: Bridging Code Graphs and Large Language Models for Better Structure-Aware Code Understanding

CGBridge is a novel framework designed to enhance the code understanding capabilities of Large Language Models (LLMs) by integrating rich structural information from code graphs. Our approach follows ...
GitHub

Graph Neural Prompting with Large Language Models

Large language models (LLMs) have shown remarkable generalization capability with exceptional performance in various language modeling tasks. However, they still exhibit inherent limitations in ...