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Yangyang Luo

3 accepted papers

2025

A Systematic Exploration of Knowledge Graph Alignment with Large Language Models in Retrieval Augmented Generation

AAAI 2025technical

Retrieval Augmented Generation (RAG) with Knowledge Graphs (KGs) is an effective way to enhance Large Language Models (LLMs). Due to the natural discrepancy between structured KGs and sequential LLMs, KGs must be linearized to text before being inputted into LLMs, leading to the problem of KG Alignm…

2024

KG-Adapter: Enabling Knowledge Graph Integration in Large Language Models through Parameter-Efficient Fine-Tuning

ACL 2024findings

Although large language models (LLMs) show remarkable capabilities and generalizability across various tasks, they are criticized for lack of expertise. One promising solution is to combine knowledge graphs (KGs) with LLMs, and recent studies focus on integrating KGs into LLMs through prompt-based m…

2023

Explicit Alignment and Many-to-many Entailment Based Reasoning for Conversational Machine Reading

EMNLP 2023long findings

Conversational Machine Reading (CMR) requires answering a user's initial question through multi-turn dialogue interactions based on a given document. Although there exist many effective methods, they largely neglected the alignment between the $\textit{document}$ and the $\textit{user-provided infor…

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