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How do I enhance LLM explainability by using KGs? (2.2 – Answer Verification) – length: up to one page

Lead: Daniel Burkhardt

Draft from Daniel Burkhardt

Problem Statement (one paragraph) 

KG-Enhanced LLM Interpretability refers to the use of knowledge graphs explainability focuses on integrating structured knowledge from Knowledge Graphs (KGs) to improve the transparency and explainability of large language models (LLMs. By integrating structured knowledge from KGs, LLMs can generate more interpretable outputs, providing justifications and factual accuracy checks for their responses. This integration helps in aligning LLM-generated knowledge with factual data, enhancing trust and reliability.). The primary goal is to allow LLMs to generate outputs that are more transparent and justifiable. This integration involves aligning LLM outputs with verifiable facts and structured data, enabling improved trust and factuality. The combination of KGs with LLMs ensures that the output remains grounded in known data, reducing issues [1,2]. 

Explanation of concepts 

  • KG Alignment with LLMs: This refers to ensuring that the representations generated by LLMs are in sync with the structured knowledge found in KGs. For example, frameworks like GLaM fine-tune LLMs to align their outputs with KG-based knowledge, ensuring that responses are factually accurate and well-grounded in known data [3]. By aligning LLMs with structured knowledge, the explainability of model predictions is improved, making it easier for users to verify how and why certain information was provided [1].

  • KG-Guided Explanation Generation: KGs assist in generating explanations for LLM outputs by providing a logical path or structure to the answer. By referencing entities and their relationships within a KG, LLMs can produce detailed, justifiable answers. Studies like those in the education domain use KG data to provide clear, factually supported explanations for LLM-generated responses [2,5].

  • Factuality and Verification: Factuality in LLM outputs is critical for trust, and KGs play a crucial role in verifying the truthfulness of LLM answers. Systems like GraphEval [6] analyze LLM outputs by comparing them to large-scale KGs, ensuring that the content is factual. This verification step mitigates hallucination risks and ensures outputs are reliable [6,7].

  • Analysis of https://github.com/zjukg/KG-LLM-Papers?tab=readme-ov-file#resources-and-benchmarking 
  • Overview of methods for LLM probing https://ar5iv.labs.arxiv.org/html/2309.01029 
  • KG Alignment 
  • KG-guided Explanation Generation 
    • Factuality and Verification https://arxiv.org/abs/2404.00942

Brief description of the state of the art (one paragraph) 

References

Answer 1: Measuring KG Alignment in LLM Representations

Draft from Daniel Burkhardt: 

Short definition/description of this topic: This involves evaluating how well the representations generated by LLMs align with the structured knowledge in KGs. This alignment is crucial for ensuring that LLMs can accurately incorporate and reflect the relationships and entities defined in KGs, thereby improving the factuality and coherence of their outputs.

literature: https://arxiv.org/abs/2311.06503 , https://arxiv.org/abs/2406.03746, https://arxiv.org/abs/2402.06764

Answer 2: KG-Guided Explanation Generation

Draft from Daniel Burkhardt: 

Short definition/description of this topic: KG-Guided Explanation Generation uses knowledge graphs to provide explanations for the outputs of LLMs. By leveraging the structured data and relationships within KGs, this approach can generate detailed and contextually relevant explanations, enhancing the interpretability and transparency of LLM outputs. 

literature: https://arxiv.org/abs/2312.00353, https://arxiv.org/abs/2403.03008

Contributors:

Answer 3: KG-Based Fact-Checking and Verification

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. 

  • 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

How do I enhance LLM reasoning through KGs? (2.3 – Answer Augmentation) – length: up to one page

Lead: Daniel Burkhardt

Draft from Daniel Burkhardt: 

Problem Statement (one paragraph) 

KG-Enhanced LLM Reasoning refers to the use of knowledge graphs to improve the reasoning capabilities of LLMs. By incorporating structured knowledge, LLMs can perform more complex reasoning tasks, such as multi-hop reasoning, where multiple pieces of information are connected to derive a conclusion.

Explanation of concepts 

Brief description of the state of the art (one paragraph) 

References

Answer 1: KG-Guided Multi-hop Reasoning

Contributors:

  • Daniel Burkhardt (FSTI)
  • Daniel Baldassare (doctima)

Short definition/description of this topic: This involves using knowledge graphs to facilitate multi-hop reasoning, where LLMs connect multiple entities and relationships to answer complex questions. This approach enhances the reasoning depth of LLMs by providing a structured path through interconnected data points in KGs.

The integration of KGs into LLM explainability is currently focused on improving transparency through KG alignment and post-hoc verification methods. Existing models like GPT-3 and GPT-4 demonstrate high linguistic ability but suffer from opacity and factual inaccuracies. Research efforts, including KG-guided explanation generation and factual verification techniques (e.g., FACTKG [5]), aim to address these challenges by incorporating KGs during and after LLM inference [1,6,5].

References

  1. Zhang et al., 2024, " Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering" 
  2. Zhao et al., 2023, "Explainability for Large Language Models: A Survey" 
  3. Dernbach et al., 2024, "GLaM: Fine-Tuning Large Language Models for Domain Knowledge Graph Alignment via Neighborhood Partitioning and Generative Subgraph Encoding" 
  4. Rasheed et al., 2024, "Knowledge Graphs as Context Sources for LLM-Based Explanations of Learning Recommendations" 
  5. Kim et al., 2023, "FACTKG: Fact Verification via Reasoning on Knowledge Graphs" 
  6. Liu et al., 2024, "Evaluating the Factuality of Large Language Models using Large-Scale Knowledge Graphs" 
  7. Hao et al., 2024, "ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings" 
  8. Jiang et al., 2023, "Efficient Knowledge Infusion via KG-LLM Alignment" 

Answer 1: Measuring KG Alignment in LLM Representations

Measuring the alignment between LLM representations and KGs involves comparing how well the LLM’s output matches the structured knowledge in the KG. For example, in GLaM [3], fine-tuning is performed to align LLM outputs with KG-derived entities and relationships, ensuring that responses are not only accurate but also interpretable. The alignment helps reduce issues like hallucinations by grounding responses in verifiable data. This method was used to improve performance in domain-specific applications, where LLMs need to accurately reflect relationships and entities defined in KGs [3, 4, 5].


Answer 2: KG-Guided Explanation Generation

KGs can be used to guide the explanation generation process, where the LLM references structured data in the KG to justify its output. For instance, in the educational domain, explanations are generated using semantic relations from KGs to ensure that recommendations and answers are factually supported [4]. This method not only provides the user with an understandable explanation but also reduces hallucination risks by ensuring that every output can be traced back to a known fact in the KG. Studies on KG-guided explanation generation in various fields confirm its utility in making LLM outputs more transparent and understandable to non-experts [4,5].

Answer 3: KG-Based Fact-Checking and Verification

KG-based fact-checking is an essential method for improving LLM explainability. By cross-referencing LLM outputs with structured knowledge in KGs, fact-checking systems like GraphEval ensure that generated responses are accurate and grounded in truth [6, 7]. This is especially useful for reducing hallucinations. GraphEval automates the process of verifying LLM outputs against a KG containing millions of facts, allowing for scalable and efficient fact-checking that improves both explainability and user trust [6].

  • 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

How do I enhance LLM reasoning through KGs? (2.3 – Answer Augmentation) – length: up to one page

Lead: Daniel Burkhardt

Problem Statement (one paragraph) 

KG-Enhanced LLM Reasoning improves the reasoning capabilities of LLMs by leveraging structured knowledge from KGs. This allows LLMs to perform more complex reasoning tasks, such as multi-hop reasoning, where multiple entities and relationships need to be connected to answer a query. Integrating KGs enhances the ability of LLMs to make logical inferences and draw conclusions based on factual, interconnected data, rather than relying solely on unstructured text [9, 10].

Explanation of concepts 

  • Multi-hop Reasoning with KGs: This involves connecting different pieces of information across multiple steps using relationships stored in KGs. By structuring queries through KGs, LLMs can reason through several layers of related entities and provide accurate answers to more complex questions [11, 10].

  • Tool-Augmented Reasoning: LLMs can use external tools, such as KG-based queries, to retrieve relevant data during inference, allowing for improved reasoning. ToolkenGPT [13] demonstrates how augmenting LLMs with such tools during multi-hop reasoning helps them perform more logical, structured reasoning by accessing real-time KG data [7, 13].

  • Consistency Checking in Reasoning: KG-based consistency checking ensures that LLMs maintain logical coherence throughout their reasoning processes. Systems like KONTEST [12] systematically test LLM outputs against KG facts to ensure that answers remain consistent with established knowledge, reducing logical errors [12, 13].

Brief description of the state of the art (one paragraph) 

The use of KGs to enhance reasoning is advancing rapidly, with multi-hop reasoning and retrieval-augmented generation (RAG) methods emerging as key techniques. These methods allow LLMs to perform reasoning tasks that require connecting multiple pieces of information through structured KG paths [9, 11]. Furthermore, systems like ToolkenGPT [13] integrate KG-based tools during inference, allowing LLMs to access external factual data, improving their reasoning accuracy [10, 13].

References

9. Liao et al., 2021, "To hop or not, that is the question: Towards effective multi-hop reasoning over knoweldge graphs" 

10. Schick et al., 2023, "Toolformer: Language Models Can Teach Themselves to Use Tools" 

11. Bratanič et al., 2024, "Knowledge Graphs & LLMs: Multi-Hop Question Answering " 

12. Rajan et al., 2024, "Knowledge-based Consistency Testing of Large Language Models" 

13. Hao et al., 2024, "ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings" 

Answer 1: KG-Guided Multi-hop Reasoning

Multi-hop reasoning refers to the process of connecting multiple entities or facts across a KG to answer complex queries. Using KGs in this way allows LLMs to follow logical paths through the data to derive answers that would be challenging with unstructured text alone. For instance, the Neo4j framework enhances LLM multi-hop reasoning by allowing the LLM to query interconnected entities efficiently [11]. This method improves LLM performance in tasks requiring stepwise reasoning across multiple facts [9, 11].literature: https://neo4j.com/developer-blog/knowledge-graphs-llms-multi-hop-question-answering/, https://link.springer.com/article/10.1007/s11280-021-00911-5


Answer 2: KG-Based Consistency Checking in LLM Outputs

Contributors:

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KG-Based Consistency Checking involves using knowledge graphs to ensure the consistency of LLM outputs. By comparing generated content with the structured data in KGs, this method can identify inconsistencies and improve the coherence of LLM-generated information.

literature:https://www.researchgate.net/publication/382363779_Knowledge-based_Consistency_Testing_of_Large_Language_ModelsKG-based consistency checking ensures that LLMs produce logically coherent and accurate outputs by comparing their answers with facts from a KG. KONTEST is an example of a system that uses KGs to systematically generate consistency tests, ensuring that LLM outputs are verified for logical consistency before being returned to the user [12]. This reduces errors in reasoning and improves the reliability of the model’s conclusions [12, 13].


How do I evaluate LLMs through KGs? (3) – length: up to one page

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