Question Answering

Contributors:

• Diego Collarana (FIT)
• Michael Wetzel (Coreon)
• Daniel Baldassare (doctima)
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Draft from Daniel Baldassare 

Definition/Description: Question answering is one of the main application field of large language models. Knowledge graphs have been shown to be useful for providing answers to questions about specific domains and improving the performance of LLM. This chapter covers:

• Methods to retrieve relevant data in the KG
• Methods to rerank the retrieved data from the KG
• Advanced prompt techniques like Chain-of-Thought, Tree of Thoughts (ToT) and  Graph of Thoughts (GoT) to handle complex questions

Literature:

Towards Improving the Performance of Question Answering System using Knowledge Graph - A Survey

Graph of Thoughts: Solving Elaborate Problems with Large Language Models

Fact Checking

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• Diego Collarana (FIT)
• Daniel Baldassare (doctima)
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Draft from Daniel Baldassare 

Definition/Description: Fact checking deals with the assertion of llm output's truthfulness.

Two main methods:

• Retrieve facts from KG (RAG- Approach)
• Train custom checker model augmented with knowledge from KG

Literature:

Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity (arxiv.org)

Fake News Detection

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Explainability

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• Diego Collarana (FIT)
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