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Contributors:
- Daniel Burkhardt (FSTI)
- Robert David (SWC)
- ...
Draft from Daniel Burkhardt:
literatur: https://arxiv.org/abs/2404.00942, https://aclanthology.org/2023.acl-long.895.pdf, https://arxiv.org/pdf/2406.01311
Description:
Short definition/description of this topic: This involves using knowledge graphs to verify the factual accuracy of information generated by LLMs. By cross-referencing LLM outputs with the structured data in KGs, this approach can identify and correct inaccuracies, ensuring that the generated information is reliable and trustworthy. literatur: https://arxiv.org/abs/2404.00942, https://aclanthology.org/2023.acl-long.895.pdf, https://arxiv.org/pdf/2406.01311
- First, the generated output is analysed regarding the knowledge graph and key entities and relationships are extracted to create a graph representation of the LLM answer.
- Next, this graph representation is then analyzed regarding the knowledge graph used for retrieval and any knowledge models in the background are also included. The analysis retrieves a graph representation of an explanation or justification and is returned as a (sub)graph or graph traversal with any additional information added, like RDF* weights.
- Finally, the explanation is then returned to the user in a human-readable way to be cross-checked with the LLM generated answer.
Considerations:
- Limited input data: a short LLM generated answer poses a challenge to effectively backtrack sufficient information in the knowledge graph for a high-quality explanation.
- Presentation: the explanation is graph-based data and difficult to explain or present to non-experts.
Standards and Protocols:
Query languages
Path retrieval
- https://graphdb.ontotext.com/documentation/10.7/graph-path-search.html
- https://neo4j.com/docs/graph-data-science/current/algorithms/pathfinding/
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