Semanlink - Arxiv Doc ; Knowledge Graphs AND LLM

[2311.07509] A Benchmark to Understand the Role of Knowledge Graphs on Large Language Model's Accuracy for Question Answering on Enterprise SQL Databases

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2023-11-21 About

[2309.15427] Graph Neural Prompting with Large Language Models

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> 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

[2306.08302] Unifying Large Language Models and Knowledge Graphs: A Roadmap

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2023-06-18 About

[2304.02711] Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learning

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2023-04-07 About