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LLMs have demonstrated impressive capabilities in generating human-like responses across diverse applications. However, their internal workings remain opaque, posing challenges to explainability and interpretability [2]. This lack of transparency introduces risks, especially in high-stakes applications, where LLMs may produce outputs with factual inaccuracies—commonly known as hallucinations—or even harmful content due to misinterpretation of prompts [6, 9]. Consequently, there is a pressing need for enhanced explainability in LLMs to ensure the accuracy, trustworthiness, and accessibility of model outputs for both end-users and researchers alike [2, 4].

One promising approach to improving LLM explainability is integrating KGs, which provide structured, fact-based representations of knowledge. KGs store relationships between entities in a networked format, enabling models to reference explicit connections between concepts and use these as reasoning pathways in generating text [10]. By aligning LLM responses with verified facts from KGs, we aim to reduce hallucinations and create outputs grounded in reliable data. For example, multi-hop reasoning over KGs can improve consistency by allowing LLMs to draw links across related entities—a particularly valuable approach for complex, domain-specific queries [11]. Additionally, retrieval-augmented methods that incorporate KG triplets can further enhance the factuality of LLM outputs by directly integrating structured knowledge into response generation, thereby minimizing unsupported claims [3].

However, the integration of KGs with LLMs presents unique challenges, particularly in terms of scalability and model complexity. The vast number of parameters in LLMs makes interpreting and tracing decision paths challenging, especially when the model must align with external knowledge sources like KGs [8]. Traditional interpretability methods, such as feature attribution techniques like SHAP and gradient-based approaches, are computationally intensive and less feasible for models with billions of parameters [2, 12]. Therefore, advancing KG-augmented approaches is essential for creating scalable, efficient solutions for real-world applications.

The need for KG-augmented LLMs is especially critical in domain-specific contexts, where high-fidelity, specialized information is essential. In fields such as medicine and scientific research, domain-specific KGs provide precise and contextually relevant information that general-purpose KGs cannot match [1]. Effective alignment with these KGs would not only support more accurate predictions but also enable structured, explainable reasoning, thereby making LLMs’ decision-making processes transparent and accessible for both domain experts and general users alike.

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 (post-hoc) 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]. Some approaches involve equipping LLMs with tools, such as fact-checking systems, that reference KG data for verifying outputs post-generation. Through this process, known as post-hoc explanation, LLMs can justify or clarify responses by referencing relevant facts from KGs, enhancing user trust and transparency. This augmentation allows LLMs to provide clearer justifications and improve the credibility of their outputs by aligning with trusted knowledge sources [7].

• Domain-Specific Knowledge Enhancement: In specialized fields like medicine or science, domain-specific KGs provide high-fidelity information that general-purpose KGs cannot offer. Leveraging these specialized KGs, LLMs can generate responses that are both contextually relevant and reliable, meeting the specific knowledge needs of domain experts. This alignment with specialized KGs is critical to ensuring that outputs are appropriate for expert users and rooted in precise, authoritative knowledge [1].

• Factuality and Verification: Knowledge Graphs (KGs) provide structured, factual knowledge that serves as a grounding source for LLM outputs. By referencing verified relationships between entities, KGs help reduce the occurrence of hallucinations and ensure that responses are factually accurate. This grounding aligns LLM outputs with established knowledge, which is essential in high-stakes fields where accuracy is critical. 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 [2,6,7].

Brief description of the state of the art

Recent research in integrating KGs with LLMs has produced several frameworks and methodologies designed to enhance model transparency, factuality, and domain relevance. Key initiatives include KG alignment and post-hoc verification techniques, both of which aim to improve the explainability and reliability of LLM outputs.

For KG alignment, approaches such as the GLaM framework fine-tune LLMs to align responses with KG-based knowledge. This ensures that model outputs remain factually grounded, particularly by embedding KG information into the LLM’s representation space. GLaM has demonstrated that aligning model outputs with structured knowledge can reduce factual inconsistencies, supporting applications that require reliable, fact-based answers [3].

In post-hoc explanation generation, frameworks like FACTKG leverage KG data to verify model responses after generation, producing detailed justifications that reference specific entities and relationships. This KG-guided approach has shown efficacy in fields like education, where models need to generate clear, factually supported answers to complex questions. FACTKG’s methodology enables LLMs to produce explanations that are both traceable and verifiable, thereby improving user trust in the generated content [5].

In domain-specific contexts, specialized KGs provide high-fidelity information that general-purpose KGs cannot. For instance, in the medical domain, projects like KnowPAT have incorporated domain-specific KGs to enhance LLM accuracy in delivering contextually appropriate responses. By training LLMs with healthcare-specific KGs, KnowPAT enables models to provide precise, authoritative responses that align with expert knowledge, which is crucial for sensitive fields where general-purpose knowledge may be insufficient [1].

Further, initiatives such as GraphEval underscore the role of KGs in factuality and verification. By analyzing LLM outputs through comparisons with large-scale KGs, GraphEval ensures that model responses align with known, structured facts, helping mitigate hallucination risks. This comparison process has proven valuable in high-stakes fields, as it enables verification of LLM-generated information against a vast repository of established facts, making outputs more reliable and reducing potential inaccuracies [6].

Answer 1: Measuring KG Alignment in LLM Representations

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Lead: Daniel Burkhardt

Problem Statement 

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 Integrating KGs with LLMs offers promising enhancements for reasoning, yet it introduces several critical challenges. One primary issue is the complexity of 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 

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].

Answer 1: KG-Guided Multi-hop Reasoning

Description: 

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].

Considerations

Standards and Protocols and Scientific Publications

Answer 2: KG-Based Consistency Checking in LLM Outputs

Description: 

KG-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].

Considerations

Standards and Protocols and Scientific Publications

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" 

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which demands navigating multiple KG relationships; this process is computationally intensive and error-prone for LLMs, as they require repeated calls for each step along the reasoning path. This accumulation of errors, combined with extensive computational demands, makes it difficult to maintain accuracy and efficiency in multi-hop queries​​ [1, 6, 7]​.

Scalability and adaptability further complicate the integration, as real-world KGs are often vast and incomplete, requiring costly adaptations that may not generalize well across different domains. This lack of adaptability limits LLM performance on task-specific KGs and can lead to knowledge gaps in complex or specialized domains​​​. Additionally, LLMs are prone to hallucinations—generating plausible but incorrect outputs—particularly when KG paths are misaligned with the models’ internal representations. This misalignment leads to factual inaccuracies and inconsistencies, a significant risk in domains that require precision, such as healthcare​​​ [8, 9]. 

Efficiency in retrieval and processing also poses a challenge, as KG-augmented models need to filter relevant data without overwhelming the LLM with unnecessary information, and standard retrieval methods often struggle with the interconnected data in KGs​​ [3, 7]. 

Finally, ensuring interpretability is a key challenge. While KGs can support transparent reasoning by providing structured pathways, integrating these with LLMs in a way that produces human-understandable rationales remains difficult. Current methods often fall short of providing coherent, traceable explanations, affecting the trustworthiness of LLM-based outputs​​. These challenges underscore the need for further research to address the computational, scalability, and interpretability limitations of KG-augmented LLM reasoning systems​ [9, 10]​.

Explanation of concepts 

• Multi-hop Reasoning with KGs: Multi-hop reasoning involves traversing several relationships within a KG to connect multiple pieces of information. By structuring queries across these relational "hops," LLMs can access layered knowledge, making it possible to answer complex questions that require linking distant but related entities. This is particularly useful for in-depth domain-specific queries where multiple steps are essential to arrive at accurate answers​​ [1, 6].

• Tool-Augmented Reasoning: In tool-augmented reasoning, LLMs integrate external resources like KGs to aid in decision-making during reasoning. Models like ToolkenGPT [5] leverage tool-based embeddings, allowing the LLM to perform dynamic, real-time KG queries. This augmentation enables LLMs to retrieve structured knowledge that informs reasoning paths and aids logical, stepwise problem-solving​​ [2, 5].

• Consistency Checking in Reasoning: Consistency checking ensures that LLMs adhere to logical coherence throughout their reasoning process. By systematically cross-referencing LLM outputs with KG facts, systems like KONTEST [4] can evaluate the alignment of generated answers with established knowledge, identifying logical inconsistencies that may arise during reasoning. This reduces contradictions and improves the factual reliability of responses​​. [4, 5].

• Chain of Thought (CoT) Reasoning Enhanced by KGs: CoT reasoning, when combined with KGs, supports a structured multi-step reasoning process. By organizing KG-based reasoning paths in a sequence, LLMs can maintain logical flow and improve interpretability in complex queries. This structured reasoning, enabled by tracing relationships in the KG, enhances transparency in decision-making by allowing users to follow the steps that led to the final answer​ [3, 7]​.

• Graph-Constrained Reasoning: In graph-constrained reasoning, LLMs are guided by the constraints of the KG, which restricts possible reasoning paths to those that align with verified entity relationships. By adhering to KG-encoded logical structures, LLMs can reduce spurious or unrelated associations, focusing only on reasoning paths that conform to the graph’s factual framework. This enhances logical accuracy and minimizes errors in multi-step reasoning​​ [11, 12].

Brief description of the state of the art 

The current landscape for enhancing LLM reasoning with KGs is advancing with several state-of-the-art methods and tools. Multi-hop reasoning and RAG are foundational techniques enabling LLMs to connect multiple pieces of information across KG paths, facilitating answers to complex, layered questions​​. Multi-hop approaches, like Paths-over-Graph (PoG), dynamically explore reasoning paths in KGs, integrating path pruning to optimize the reasoning process and focus on relevant data​​ [1, 6, 7, 11]. 

Another significant development is tool-augmented reasoning, exemplified by systems such as ToolkenGPT, which equips LLMs with the ability to access and utilize KG-based tools during inference. ToolkenGPT creates embeddings for external tools (referred to as "toolkens"), enabling real-time KG lookups that aid in logical, structured reasoning by supplementing the LLM's outputs with factual data drawn directly from KGs​​. Similarly, Toolformer offers dynamic API-based access to KG data, facilitating reasoning with external support for tasks requiring specific, fact-based insights​ [2, 5].

Consistency-checking frameworks are also essential for enhancing reasoning accuracy. Systems like KONTESTevaluate LLM outputs against KG facts, ensuring logical coherence and flagging inconsistencies. This method reduces the logical errors LLMs might otherwise produce in reasoning tasks by cross-referencing generated answers with verified KG knowledge​​. Furthermore, GraphEval is used to assess the factuality of LLM responses, leveraging a judge model that systematically aligns generated answers with KG-derived facts​ [4, 13, 14].

Chain of Thought (CoT) reasoning combined with KGs enables LLMs to approach reasoning tasks in a multi-step, structured manner. By organizing KG-based reasoning paths into sequential steps, CoT supports transparency and traceability in complex queries, particularly useful for answering multi-entity questions​​. Lastly, graph-constrained reasoning, as seen in frameworks like Graph-Constrained Reasoning and PoG, directs LLM reasoning within predefined KG paths, minimizing irrelevant associations and enhancing logical consistency by adhering to factual constraints within the graph structure​​ [3, 7, 11, 12].

Answer 1: KG-Guided Multi-hop Reasoning

Description: 

KG-guided multi-hop reasoning enables Large Language Models (LLMs) to connect multiple entities or facts across a Knowledge Graph (KG) to address complex queries. By utilizing KGs, LLMs can follow structured, logical paths through interconnected data, which helps them generate answers that would be challenging to derive from unstructured data sources. Multi-hop reasoning allows the LLM to step through relevant nodes within the KG, effectively using each "hop" to refine its understanding and response. For example, the Neo4j framework supports multi-hop reasoning by allowing LLMs to query interconnected entities efficiently, which enhances performance on tasks that require detailed, stepwise reasoning across multiple facts and relationships​​. Additionally, models like Paths-over-Graph (PoG) leverage KGs to perform dynamic multi-hop path exploration, optimizing data retrieval by pruning irrelevant information, thus helping the LLM access only the most relevant paths needed for accurate answers​ [1, 3, 7, 11].

Considerations

1. Path Optimization: Not all multi-hop paths within a KG are equally relevant. Applying path optimization techniques, like pruning irrelevant nodes and paths, ensures that LLMs focus on the most pertinent information, reducing computational overhead and enhancing answer accuracy [1, 6].
2. Data Quality and Completeness: The reliability of multi-hop reasoning depends heavily on the quality and completeness of the KG. Incomplete or noisy data can lead to erroneous inferences or missed connections, so it is essential to maintain an accurate and well-curated KG [1, 6].
3. Scalability and Efficiency: KGs, especially large-scale ones, can be computationally intensive to query in multi-hop settings. Efficient query mechanisms and algorithms are crucial to minimize latency and enhance the overall responsiveness of the LLM [1, 6].
4. Error Accumulation in Multi-hop Paths: With each additional hop, the likelihood of error increases, especially if the KG contains outdated or incorrect information. Error correction techniques, such as consistency checks, can help maintain the quality of multi-hop reasoning [1, 6].

Standards and Protocols and Scientific Publications

• Query Languages: Specialized languages streamline querying within KGs, essential for multi-hop reasoning tasks.
  • SPARQL for RDF-based KGs: https://www.w3.org/TR/sparql11-query/​.
  • Cypher for Neo4j-based KGs: https://neo4j.com/product/cypher-graph-query-language/
• Path Retrieval Algorithms: Efficient path retrieval algorithms enable LLMs to perform multi-hop reasoning by identifying and navigating relevant paths within KGs.
  • Ontotext GraphDB Path Retrieval: https://graphdb.ontotext.com/documentation/10.7/graph-path-search.html#graph-path-search
  • Neo4j Pathfinding Algorithms: https://neo4j.com/docs/graph-data-science/current/algorithms/pathfinding/

Answer 2: KG-Based Consistency Checking in LLM Outputs

Description: 

KG-based consistency checking is a method to enhance the accuracy and logical coherence of LLM outputs by cross-referencing generated answers with structured facts in a Knowledge Graph (KG). This approach helps to ensure that the information LLMs provide aligns with verified knowledge. Systems like KONTEST exemplify this method by systematically using KGs to generate consistency tests, checking the logical validity of LLM outputs before presenting them to users. By evaluating LLM responses against established facts, KONTEST reduces errors in reasoning and enhances the reliability and trustworthiness of model-generated conclusions​​. Additionally, the use of consistency-checking frameworks like GraphEval allows for scalable verification, applying KG-based facts to systematically evaluate and align LLM outputs, which further mitigates inaccuracies​ [4, 13, 14].

Considerations

• Data Completeness and Accuracy: The effectiveness of consistency checking depends on the completeness and accuracy of the KG. Gaps or inaccuracies in the KG can lead to incorrect assessments of LLM outputs, so maintaining a high-quality KG is essential [4, 13, 14].
• Computational Overhead: Consistency checking involves comparing multiple elements within the LLM response against the KG, which can introduce significant computational costs, especially for large KGs or high-frequency queries [4, 13, 14].
• Contextual Matching: For effective consistency checks, it’s crucial that the KG context aligns with the LLM's response context. Misalignment may result in false positives or negatives in consistency assessments, affecting accuracy [4, 13, 14].
• Human-Readable Output: Consistency checks often require translating graph-based verification results into explanations that are accessible to non-expert users, particularly in sensitive applications where explainability is critical​​​ [4, 13, 14].

Standards and Protocols and Scientific Publications

• KONTEST Testing Protocol: Used to ensure logical consistency of outputs by cross-verifying LLM results with KG data​ [4]​.
• GraphEval for Automated Consistency Testing: Applies large-scale KGs to systematically assess the factuality and consistency of LLM responses [13]​.

References

1. Liao et al., 2021, "To hop or not, that is the question: Towards effective multi-hop reasoning over knoweldge graphs" 
2. Schick et al., 2023, "Toolformer: Language Models Can Teach Themselves to Use Tools" 
3. Bratanič et al., 2024, "Knowledge Graphs & LLMs: Multi-Hop Question Answering " 
4. Rajan et al., 2024, "Knowledge-based Consistency Testing of Large Language Models" 
5. Hao et al., 2024, "ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings" 
6. Choudhary, N., & Reddy, C. K. (2024). Complex Logical Reasoning over Knowledge Graphs using Large Language Models
7. Jiang, B., et al. (2024). Reasoning on Efficient Knowledge Paths: Knowledge Graph Guides Large Language Model for Domain Question Answering​.
8. Ding, R., et al. (2023). A Unified Knowledge Graph Augmentation Service for Boosting Domain-specific NLP Tasks
9. Wang, S., et al. (2023). Unifying Structure Reasoning and Language Pre-training for Complex Reasoning Tasks
10. Zhao et al., 2023, "Explainability for Large Language Models: A Survey" 
11. Tan, X., et al. (2024). Paths-over-Graph: Knowledge Graph Empowered Large Language Model Reasoning
12. Akirato/LLM-KG-Reasoning GitHub repository (2023). Graph-Constrained Reasoning​.
13. Liu et al., 2024, "Evaluating the Factuality of Large Language Models using Large-Scale Knowledge Graphs" ,
14. Lo, P-C., et al. (2023). On Exploring the Reasoning Capability of Large Language Models with Knowledge Graphs(2312.00353v1)​.

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

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