Semanlink - AI@Amazon AND Arxiv Doc

[2309.15427] Graph Neural Prompting with Large Language Models

Tags:

> Can we learn beneficial knowledge from KGs
and integrate them into pre-trained LLMs?

> we propose to
leverage the factual knowledge from KGs to enhance LLMs,
while still benefiting from circumventing the burdensome
training expenses by using pre-trained LLMs

> Graph Neural Prompting
(GNP), a plug-and-play method to assist pre-trained
LLMs in learning beneficial knowledge from KGs
>
> GNP
encodes the pertinent grounded knowledge and complex
structural information to derive Graph Neural Prompt, an
embedding vector that can be sent into LLMs to provide
guidance and instructions

> - GNP first utilizes
a GNN to capture and encode the
intricate graph knowledge into **entity/node embeddings**. 
> - Then,
a cross-modality pooling module is present to determine
the **most relevant node embeddings in relation to the text
input**, and consolidate these node embeddings into **a holistic
graph-level embedding**.
> - After that, GNP encompasses a
**domain projector** to bridge the inherent disparities between
the graph and text domains.
> - Finally, a **self-supervised link
prediction objective** is introduced to enhance the model
comprehension of relationships between entities and capture
graph knowledge in a self-supervised manner.

2023-09-28 About

[2004.05119] Beyond Fine-tuning: Few-Sample Sentence Embedding Transfer

Tags:

2022-03-31 About

[2110.10778] Contrastive Document Representation Learning with Graph Attention Networks

Tags:

2022-03-10 About

[2004.11892] Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering

Tags:

2022-02-11 About

[1807.08447] LinkNBed: Multi-Graph Representation Learning with Entity Linkage

Tags:

2020-05-11 About