KG-Enhanced LLM Training
Integrating KGs into Training Objective
Contributors:
- Diego Collarana (FIT)
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Integrating KGs into LLM Inputs (verbalize KG for LLM training)
Contributors:
- Diego Collarana (FIT)
- Daniel Baldassare (doctima)
- Michael Wetzel (Coreon)
- Sabine Mahr (word b sign)
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Integrating KGs by Fusion Modules
Contributors:
- Diego Collarana (FIT)
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Retrieval-Augmented Generation (RAG)
Draft Daniel Burkhardt :
- Definition of RAG
- Types of RAG
- Applications for RAG
KG-Guided Retrieval Mechanisms
Contributors:
- Daniel Burkhardt (FSTI)
- Robert David (SWC)
- Diego Collarana (FIT)
- Daniel Baldassare (doctima)
- Michael Wetzel (Coreon)
Draft Robert David:
- Initial RAG idea: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
- RAG is commonly used with vector databases.
- can only grasp semantic similarity represented in the document content
- only unstructured data
- vector distance instead of a DB search limits the retrieval capabilities
- Graph RAG uses knowledge graphs as part of the RAG system
- KGs for retrieval (directly), meaning the database is storing KG data
- KGs for retrieval via a semantic layer, potentially retrieving over different data sources of structured and unstructured data
- KGs for augmenting the retrieval, meaning the queries to some database is modified via KG data
- Via Graph RAG, we can
- ingest additional semantic background knowledge (knowledge model) not represented in the data itself
- additional related knowledge based on defined paths (rule-based inference)
- focus on certain aspects of a data set for the retrieval (search configuration)
- personalization: represent different roles for retrieval via ingesting role description data into the retrieval (especially important in an enterprise environment)
- reasoning
- linked data makes factual knowledge related to the LLM-generated knowledge and thereby provide a means to check for correctness
- explainable AI: provide justifications via KG
- consolidate different data sources: unstructured, semi-structured, structured (enterprise knowledge graph scenario)
- doing the actual retrieval via KG queries: SPARQL
- hybrid retrieval: combine KG-based retrieval with vector databases or search indexes
- ingest additional semantic background knowledge (knowledge model) not represented in the data itself
Hybrid Retrieval Combining KGs and Dense Vectors
Contributors:
- Daniel Burkhardt (FSTI)
- Diego Collarana (FIT)
- Daniel Baldassare (doctima)
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Draft from Daniel Burkhardt:
- Dense and sparse vectors
- Hybrid search
- Integration of KG with dense vectors for score combination (https://github.com/InternLM/HuixiangDou)
KG-Enhanced Re-Ranking of Retrieved Information
Contributors:
- Daniel Burkhardt (FSTI)
- Diego Collarana (FIT)
- Daniel Baldassare (doctima)
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Draft from Daniel Burkhardt
KG-Enhanced LLM Interpretability
KGs for LLM probing
KG-Based Analysis of Attention Patterns
Contributors:
- Daniel Burkhardt (FSTI)
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Measuring KG Alignment in LLM Representations
Contributors:
- Daniel Burkhardt (FSTI)
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KG-Guided Explanation Generation
Contributors:
- Daniel Burkhardt (FSTI)
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KG-Based Fact-Checking and Verification
Contributors:
- Daniel Burkhardt (FSTI)
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KG-Enhanced LLM Inference or Reasoning
KG-Guided Multi-hop Reasoning
Contributors:
- Daniel Burkhardt (FSTI)
- Daniel Baldassare (doctima)
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Integrating Symbolic Reasoning with LLMs using KGs
Contributors:
- Daniel Burkhardt (FSTI)
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KG-Based Consistency Checking in LLM Outputs
Contributors:
- Daniel Burkhardt (FSTI)
- Daniel Baldassare (doctima)
- Michael Wetzel (Coreon)
- ...
KGs for LLM Analysis
Using KGs to Evaluate LLM Knowledge Coverage
Contributors:
- Daniel Burkhardt (FSTI)
- Daniel Baldassare (doctima)
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Analyzing LLM Biases through KG Comparisons
Contributors:
- Daniel Burkhardt (FSTI)
- Daniel Baldassare (doctima)
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