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First draft to be created until 11 October 2024.

ADD NEW TOP LEVEL SECTION: LLM TRAINING

How do I enhance/augment/extend LLM training through KGs? (LLM TRAINING) – length: up to one page

Lead: Daniel Baldassare

Contributors:

  • Diego Collarana (FIT)
  • Daniel Baldassare (doctima) – Lead
  • Michael Wetzel (Coreon)
  • Rene Pietzsch (ECC)
  • Alan Akbik (HU)


Problem statement

The training of large language models typically employs unsupervised methods on extensive datasets. Despite their impressive performance on a range of tasks, these models often lack the practical, real-world knowledge required for certain applications. Furthermore, since domain-specific data is not included in the public domain datasets used for pre-training or fine-tuning large language models (LLMs), the integration of knowledge graphs (KGs) becomes fundamental for the injection of proprietary knowledge into LLMs, especially for enterprise solutions. In order to infuse this knowledge into LLMs during training, many techniques have been researched in recent years, resulting in three main state-of-the-art methods (Pan et al, 2024): 

  1. Integration of KGs into training objectives (See answer 1)
  2. Verbalization of KGs into LLM inputs (See answer 2)
  3.  Integrate KGs by Fusion Modules: Joint training of graph and language models (See answer 3)

Explanation of concepts

The first method focuses on extending the pre-training procedure. The term pretraining objectives is used to describe the techniques that guide the learning process of a model from its training data. In the context of pre-training large language models, a variety of methods have been employed based on the architecture of the model itself. Decoder-only models such as GPT-4 usually use Casual Language Modelling (CLM), where the model is presented with a sequence of tokens and learns to predict the next token in the sequence based solely on the preceding tokens (Wang et al., 2022). Integrating KGs into training objectives consists in extending the standard llm's pre-training objective of generating coherent and contextually relevant text by designing a knowledge aware pre-training.

The second method involves integrating KGs directly into the LLM's input by verbalising the knowledge graph into the prompt, thereby transforming structured data into text format that the LLM can process and learn from. Data from the knowledge is either prepended or postpended to the user's question as contextual information in the prompt. Within this approach the standard llm pre-training objective of generating coherent and contextually relevant text remains untouched and the knowledge augmentation task is modeled as a linguistic task.


Brief description of the state of the art

First draft to be created until 11 October 2024


Proposed solutions:

Answer 1: integrate KGs into the LLM Training Objective

Contributors:

  • Diego Collarana (FIT)

Short definition/description of this topic: please fill in ...

  • Content ...
  • Content ...
  • Content ... 

Answer 2: integrate KGs into LLM Inputs (verbalize KG for LLM training)

Contributors:

  • Diego Collarana (FIT)
  • Daniel Baldassare (doctima) – Lead
  • Michael Wetzel (Coreon)
  • Rene Pietzsch (ECC)
  • ... 

Draft from Daniel Baldassare :

Short definition/description of this topic: Verbalizing knowledge graphs for LLM is the task of representing knowledge graphs as text so that they can be written directly in the prompt, the main input source of LLM. Verbalization consists of finding textual representations for nodes, relationships between nodes, and their metadata. Verbalization can take place at different stages of the LLM lifecycle, during training (pre-training, instruction fine-tuning) or during inference (in-context learning), and consists in:

Answer 3: Integrate KGs by Fusion Modules

Contributors:

  • Diego Collarana (FIT)

Short definition/description of this topic: please fill in ...

  • Content ...
  • Content ...
  • Content ... 


References:

  • S. Pan, L. Luo, Y. Wang, C. Chen, J. Wang, und X. Wu, „Unifying Large Language Models and Knowledge Graphs: A Roadmap“, IEEE Trans. Knowl. Data Eng., Bd. 36, Nr. 7, S. 3580–3599, Juli 2024, doi: 10.1109/TKDE.2024.3352100.
  • T. Wang u. a., „What Language Model Architecture and Pretraining Objective Works Best for Zero-Shot Generalization?“, in Proceedings of the 39th International Conference on Machine Learning, PMLR, Juni 2022, S. 22964–22984. Zugegriffen: 3. Oktober 2024. [Online]. Verfügbar unter: https://proceedings.mlr.press/v162/wang22u.html



ADD NEW TOP LEVEL SECTION: ENHANCING LLMs AT INFERENCE TIME

How do I use KGs for Retrieval-Augmented Generation (RAG)? (2.1 – Prompt Enhancement) – length: up to one page

Lead: Diego Collarana 

Contributors:

  • Daniel Burkhardt (FSTI)
  • Robert David (SWC)
  • Diego Collarana (FIT)
  • Daniel Baldassare (doctima)
  • Michael Wetzel (Coreon)

Problem statement

RAG methods aim to enhance the capabilities of LLMs by providing real-time information and domain-specific knowledge that may not be present in their training data. Despite its advantages over standalone LLMs, Conventional RAG has the following limitations:

  1. Struggles to answer queries that require the intricate interconnectedness of information and global context crucial for generating comprehensive summaries.
  2. It cannot integrate structure and unstructured data, a use case typically required in industrial applications.
  3. Limited accuracy due to context loss during text chunking and its reliance on text similarity search.
  4. It has limited reasoning capabilities, especially with abstract questions that require reasoning, inference, or the synthesis of new information not explicitly stated in the source material.
  5. The answers cannot be backtracked to the information sources (factual grounding).
  6. The external knowledge, while consistent, can still lead to inconsistencies in the generated answer.

Explanation of concepts

  • Retrieval-augmented generation (RAG) methods combine retrieval mechanisms with generative models to enhance the output of LLMs by incorporating external knowledge. By grounding the generated output in specific and relevant information, RAG methods improve the quality and accuracy of the generated output.

  • Types of RAG:

    • Conventional RAG has three components: 1) Knowledge Base, typically created by chunking text documents, transforming them into embeddings, and storing them in a vector store. 2) Retriever searches the vector database for chunks that exhibit high similarity to the query. 3) Generator feeds the retrieved chunks, alongside the original query, to an LLM to generate the final response.
    • Graph RAG integrates knowledge graphs into the RAG framework, allowing for the retrieval of structured data that can provide additional context and factual accuracy to the generative model. 
      The retrieval can be done on any source with a semantic representation, e.g., documents with semantic annotations or relational data via OBDA or R2RML, thereby ingesting structured and unstructured source information into the Graph RAG.
  • RAG is used in various natural language processing tasks, including question-answering, information extraction, sentiment analysis, and summarization. It is particularly beneficial in scenarios requiring domain-specific knowledge.

Brief description of the state-of-the-art

The emerging field of Graph RAG develops methods to exploit the rich, structured relationships between entities within a KG to retrieve more precise, factually relevant context for LLMs [9]. Graph RAG methods encompass graph construction, knowledge retrieval, and answer-generation techniques [1,2,5]. We find methods that leverage existing open-source KGs [3] to methods for automatically building domain-specific KGs from raw textual data using LLMs [6]. The retrieval phase focuses on efficiently extracting pertinent subgraphs, paths, or nodes relevant to a user query with techniques like embedding similarity, pre-defined rules, or LLM-guided search. In the generation phase, retrieved graph information is transformed into LLM-compatible formats, such as graph languages, embeddings, or GNN encoding, to generate enriched and contextually grounded responses [4]. Recently, significant attention has been given to hybrid approaches combining conventional RAG and Graph RAG strengths [7,8]. HybridRAG integrates contextual information from traditional vector databases and knowledge graphs, resulting in a more balanced and effective system that surpasses individual RAG approaches in critical metrics like faithfulness, answer relevancy and context recall. 

We describe various solutions for integrating knowledge graphs into RAG systems to improve accuracy, reliability, and explainability. 

Answer 1: Knowledge Graph as a Database with Natural Language Queries (NLQ)

Description: This solution treats the knowledge graph as a structured database and leverages natural language queries (NLQ) to retrieve specific information. The implementation steps are as follows:

  • First, the user's question is processed to extract key entities and relationships using entity linking and relationship extraction techniques. (Natural Language Understanding)
  • Next, the natural language query is partially or fully mapped into a graph query language, e.g., Cypher or SPARQL. (Graph Query Construction)
  • Then, the constructed graph query is executed against the knowledge graph database, which retrieves precise and targeted information from the knowledge graph. (Knowledge Graph Execution)
  • Finally, the retrieved results are passed to the LLM for summarization or further processing to generate the final answer. (Response generation)

Considerations:

  • Accurate Query Mapping: Requires advanced NLP techniques to map natural language queries to graph queries accurately. Entity linking and relationship extraction must be precise to ensure correct query formulation.
  • Performance Efficiency: Executing complex graph queries may impact performance, especially with large-scale knowledge graphs. Optimization of graph databases and queries is necessary for real-time applications.
  • Scalability: The system should handle growing knowledge graphs without significant performance loss. Scalable graph database solutions are essential.
  • User Experience: The system must effectively interpret user intent from natural language inputs. Providing clear and concise answers enhances usability and trust.

Standards and Protocols:

  • Compliance with Data Standards: Ensure the knowledge graph adheres to relevant data modeling standards. Where applicable, utilize standardized vocabularies and ontologies.
  • Interoperability: Design the system for various graph databases and query languages. Support integration with external data sources and systems.

Answer 2: Knowledge Graph-Guided Retrieval Mechanisms

Description: KG-Guided Retrieval Mechanisms involve using, for example, knowledge graphs or vector databases to enhance the retrieval process in RAG systems. Knowledge graphs provide a structured representation of knowledge, enabling more precise and contextually aware information retrieval. This approach can directly query knowledge graphs or use them to augment queries to other data sources, improving the relevance and accuracy of the retrieved information.

  • First, the user's question is processed to extract key entities and relationships using entity linking and relationship extraction techniques as a (semantic) graph representation of the question. (Natural Language Understanding)
  • Next, the graph representation is executed against the knowledge graph database, which first retrieves information from the knowledge graph and then retrieves the associated mapped data source.
    Data sources can be of different kinds:
    • Knowledge graph data
    • Non-knowledge graph data with a graph representation:
      • Tabular and relational data, e.g., via OBDA or R2RML.
      • Semi-structured data, e.g., XML or DITA.
      • Unstructured natural language, e.g., via semantic annotations.
  • Then, the retrieved (different kinds of) results are consolidated (preprocessed) to be ingested into the LLM prompt. (Data consolidation)
  • Finally, the consolidated results are passed to the LLM for summarization or further processing to generate the final answer. (Response generation)

Considerations:

  • Limited input data: a short user's question poses a challenge to effectively create a graph representation sufficiently expressive for a high-quality retrieval of information.
  • Knowledge model: a high-quality graph representation of both the user question and the actual information in the database is very likely to need a knowledge model with sufficient expressivity in the background.
  • Graph representation: doing graph-based retrieval of (heterogeneous) data sources needs an established graph representation for each source.
  • Consolidation architecture: Setting up a system architecture for consolidated data sources needs different kinds of integration components.
  • Semantic gap: there is the risk of a gap of semantic information between the retrieved information and the LLM-generated answer, because any semantics contained in the knowledge graph and any knowledge model cannot be preserved during ingestion into the LLM generation.

Standards and Protocols:

  • Compliance with Data Standards: Ensure the knowledge graph adheres to relevant data modeling standards. Where applicable, utilize standardized vocabularies and ontologies.
  • Interoperability: Design the system for various graph databases and query languages. Support integration with external data sources and systems.

Answer 3: Hybrid RAG Combining KGs and Dense Vectors

Draft from Daniel Burkhardt: 

Description: Hybrid Retrieval combines the strengths of knowledge graphs and dense vector representations to improve information retrieval. This approach leverages the structured, relational data from knowledge graphs and the semantic similarity captured by dense vectors, resulting in enhanced retrieval capabilities. Hybrid retrieval systems can improve semantic understanding and contextual insights while addressing scalability and integration complexity challenges.

  • First, the user submits a query that is analyzed to select which retrieval approach (1.*) (Arbitrator or Classification)
  • The retrieval components are called either in parallel or sequentially (Hybrid Retrieval Process)
    • Vector Search: Retrieves data based on vector embeddings
    • Keyword Search: Retrieves data based on keyword matching
    • Graph Queries: Retrieves structured data from the knowledge graph
  • Then, we combine results from all retrieval methods. (Result Integration)
  • Response Generation: LLM generates and delivers the response.

Considerations:

  • Requires efficient result fusion techniques
  • Addresses diverse data types and sources
  • Increase in latency of response

REFERENCE TO BE REMOVED

References

  • [1] Boci Peng, Yun Zhu, Yongchao Liu, Xiaohe Bo, Haizhou Shi, Chuntao Hong, Yan Zhang, Siliang Tang: Graph Retrieval-Augmented Generation: A Survey. CoRR abs/2408.08921 (2024)
  • [2] Diego Collarana, Moritz Busch, Christoph Lange: Knowledge Graph Treatments for Hallucinating Large Language Models. ERCIM News 2024(136) (2024)
  • [3] Junde Wu, Jiayuan Zhu, Yunli Qi: Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generation. CoRR abs/2408.04187 (2024)
  • [4] Sen, Priyanka, Sandeep Mavadia, and Amir Saffari. Knowledge graph-augmented language models for complex question answering. Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations - NLRSE (2023)
  • [5] Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, Xindong Wu: Unifying Large Language Models and Knowledge Graphs: A Roadmap. IEEE Trans. Knowl. Data Eng. 36 (7) (2024)
  • [6] Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, Jonathan Larson: From Local to Global: A Graph RAG Approach to Query-Focused Summarization. CoRR abs/2404.16130 (2024)
  • [7] Bhaskarjit Sarmah, Benika Hall, Rohan Rao, Sunil Patel, Stefano Pasquali, Dhagash Mehta: HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction. CoRR abs/2408.04948 (2024)
  • [8] Jens Lehmann, Dhananjay Bhandiwad, Preetam Gattogi, Sahar Vahdati: Beyond Boundaries: A Human-like Approach for Question Answering over Structured and Unstructured Information Sources. Trans. Assoc. Comput. Linguistics (2024)
  • [9] Juan Sequeda, Dean Allemang, Bryon Jacob: A Benchmark to Understand the Role of Knowledge Graphs on Large Language Model's Accuracy for Question Answering on Enterprise SQL Databases. GRADES/NDA (2024)

First draft to be created until 11 October 2024.

ADD NEW TOP LEVEL SECTION: LLM TRAINING

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

Lead: Daniel Burkhardt

Problem Statement

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

Description

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].

Considerations

  • Data Quality and Coverage: The quality and completeness of the KG significantly impact alignment. If the KG lacks comprehensive data, the LLM might still produce hallucinations or incomplete outputs despite alignment efforts.
  • Alignment Metrics: Measuring alignment between LLM and KG representations requires specific metrics. These may include entity coverage, similarity scores between LLM-generated and KG-retrieved responses, or accuracy in matching relational paths in the KG.
  • Computational Complexity: Aligning LLM representations with extensive KGs is computationally intensive, especially with large-scale KGs. Efficient alignment techniques and resource allocation are crucial for scalable implementations.

Standards, Protocols and Scientific Publications

  • Embedding Alignment: Techniques like TransE and BERT-based entity alignment support embedding alignment between LLMs and KGs.
  • KG Ontologies: Ontologies help structure KG data and provide a common format, such as OWL (Web Ontology Language) and RDFS (RDF Schema).

Answer 2: KG-Guided Explanation Generation

Description

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].

Considerations

  • Interpretability for Non-Experts: Presenting KG-based explanations to lay audiences can be challenging if the KG structure is complex. Simplified language or visual aids may be necessary to enhance comprehension.
  • Semantic Completeness: The KG needs to encompass the relevant relationships to generate comprehensive explanations. Missing relationships may lead to partial or insufficient justifications.
  • Consistency Across Domains: Cross-domain use cases require consistent explanations, which may be complex if the KG includes overlapping or domain-specific relations that vary significantly.

Standards, Protocols and Scientific Publications

Answer 3: KG-Based Fact-Checking and Verification

Description: 

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: Short LLM-generated answers may lack sufficient information for thorough fact-checking. In such cases, additional context or auxiliary information may be required to validate the response accurately.
  • Presentation for Non-Experts: Graph-based explanations can be challenging to interpret for lay audiences. Translating complex graph structures into user-friendly formats or summaries can improve accessibility.
  • Data Complexity and Scale: Fact-checking across large KGs can be resource-intensive, requiring efficient query algorithms and substantial computational power for high-speed verification

Standards, Protocols and Scientific Publications:

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" 
  9. Weidinger, L., et al., 2021. "Ethical and social risks of harm from Language Models". arXiv preprint arXiv:2112.04359
  10. Liao et al., 2021, "To hop or not, that is the question: Towards effective multi-hop reasoning over knoweldge graphs" 
  11. Bratanič et al., 2024, "Knowledge Graphs & LLMs: Multi-Hop Question Answering" 
  12. Sundararajan, M., et al. (2017). "Axiomatic Attribution for Deep Networks." International Conference on Machine Learning.
  13. Bordes et al. (2013). "Translating Embeddings for Modeling Multi-relational Data".  

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

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 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" 

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

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

Lead: Fabio

Contributors:

  • Daniel Burkhardt (FSTI)
  • Daniel Baldassare (doctima)
  • Fabio Barth (DFKI)
  • Max Ploner (HU)
  • Alan Akbik (HU)
  • ...

Problem statement (only a few sentences, at most one paragraph): Automatic evaluation of LLMs is usually done by cleverly comparing a desired result. The desired output can be evaluated using direct matching or similarity metrics (BLEU, N-gram, ROUGE, BERTScore). However, there are various reasons why KG can be used in the evaluation to support or enhance these evaluation techniques.

Explanation of concepts: Firstly, KG triplets can be used to evaluate the amount of knowledge that is represented by the LLMs parameters and how consistently this knowledge can be retrieved. Secondly, KG triplets can be used to evaluate the output of an LLM. The triplets can be compared with a KG to check factuality or knowledge coverage. Examples of this knowledge coverage would be political positions, cultural or sporting events, or current news information. Furthermore, the extracted KG triplets can be used to evaluate tasks/features where a similarity comparison of the LLM output is undesirable. This is the case for identifying and evaluating hallucinations of LLMs.
The final reason is to use KGs to enhance LLM inputs with relevant information. This method is beneficial, for example, if the goal is to use in-context learning to provide relevant information for a specific task to the LLM. In addition, planned adversarial attacks can be carried out on the LLM to uncover biases or weak points. Both variants are explained in more detail below as examples. 

Brief description of the state of the art (only a few sentences, at most one paragraph): ...

Properties to evaluate the LLM on:

  • Represented Knowledge: Which fact-queries can the LLM answer correctly & consistently?
  • Factuality: When generating an output, are the facts an LLM uses in its answer correct?
  • Biases:


Answer 1: Using KGs to Evaluate LLM Represented Knowledge

Description:

Relational triples from a Knowledge Graph (KG) can be leveraged to create up-to-date and domain-specific datasets for evaluating knowledge within Language Models (LMs) [1, 2, 4, 5, 6]. The LLM can then be queried with the subject and relation to predict the object using either a question-answer pattern [6], predicting the answer using masked language modeling [4, 5], or predicting the correct statement from a multiple-choice item [1,2]. KGs not only provide the correct answers but also enable the creation of plausible distractors (incorrect answer options), allowing the generation of multiple-choice items to evaluate the knowledge represented in an LM [1, 2].

Considerations:

  • While KGs are inherently structured to maintain consistency in factual representation, LMs do not always yield consistent answers, especially when queries are rephrased [2, 3]. The integration of KGs for evaluation set generation can address this by allowing multiple phrasings of a single query, all linked to the same answer and relational triple. This approach helps measure an LM’s robustness in recognizing equivalent rewordings of the same fact [1, 2, 3].
  • When using a question-answering format (to evaluate text generating / autoregressive LMs), the free-form answer of the model needs to be compared to the reference answer [1]. While there are multiple ways of comparing the answer to the reference [1], no single approach is ideal. Multiple-choice-based approaches mitigate this problem entirely [1, 2, 6] but inherently have a limited answer space and may encourage educated guessing, simplifying the task by providing plausible options. Conversely, open-ended answers require the model to generate the correct response without cues and may align better with real-world use cases.
  • Verbalizing a triple not only requires labels for the subject and object (which are typically annotated with one or multiple labels), but a rule [1] or template [2] which translates the formal triple into natural text. This may requires humans to create one or multiple rules per relation. Depending on the target language and the number of used relations this can be a non-negligible amount of work.


Standards and Protocols and Scientific Publications:

The use of KGs for the generation of evaluation datasets has been successfully employed in various scientific publications [1, 2, 4, 5, 6, 7].


References:

  1. https://arxiv.org/pdf/2401.00761 (The Earth is Flat?)
  2. https://aclanthology.org/2024.findings-naacl.155/ (BEAR)
  3. https://arxiv.org/pdf/2204.06031 (Review)
  4. https://aclanthology.org/D19-1250/ (LAMA)
  5. https://www.akbc.ws/2022/assets/pdfs/15_kamel_knowledge_analysis_with_.pdf (KAMEL)
  6. https://aclanthology.org/N19-1421/ (CommonsenseQA)

Answer 2: Using KGs to Evaluate LLM Knowledge Coverage Factuality

Maybe add additional properties such as factuality, correctness, precision etc. or perhaps keep these that we have right now and call them "selected properties" ... (We could move the definition of these properties to the top and discuss which answer addresses which property)

Draft from Daniel Burkhardt: 

Description: This involves using knowledge graphs to analyze and evaluate various aspects of LLMs, such as knowledge coverage and biases. KGs provide a structured framework for assessing how well LLMs capture and represent knowledge across different domains. This involves assessing the extent to which LLMs cover the knowledge represented in KGs. By comparing LLM outputs with the structured data in KGs, this approach can identify gaps in knowledge and areas for improvement in LLM training and performance

(First Version): The first evaluation process can be divided into two parts. Those can be executed through various techniques, which this section will not discuss. First, the LLM generates output sequences based on an evaluation set of input samples. Specific KG triplets are then identified and extracted from the generated output sequence. The variants for extraction and identification can be found in other subchapters of this DIN SPEC. The extracted KG triplets are usually domain or task-specific. These KG triplets are used to generate a KG. 
In the second step, the KG can now be analyzed. For instance, factuality can be checked by analyzing each KG triplet in the generated KG, given the context provided. Alternatively, the extracted KG triplets can be compared with an existing, more extensive KG to analyze the knowledge coverage of an LLM.

- Considerations:
- Standards and Protocols and Scientific Publications:

References:

  1. https://www.amazon.science/publications/grapheval-a-knowledge-graph-based-llm-hallucination-evaluation-framework


Answer 3: Analyzing LLM Biases through KG Comparisons

Draft from Daniel Burkhardt: 

Description: This involves using knowledge graphs to identify and analyze biases in LLMs. By comparing LLM outputs with the neutral, structured data in KGs, this approach can highlight biases and suggest ways to mitigate them, leading to more fair and balanced AI systems.

First Version: In the second process, the inputs, i.e., the evaluation samples, are enhanced with information from a KG to provide helpful or misleading context. KG nodes must first be extracted from the samples using, for example, RAG. Then, based on the extracted KG nodes, the top k nodes can be determined from the KG using an arbitrarily efficient retrieval method. These nodes can then be used to enhance the input. For example, the nodes can be displayed as “superior knowledge” in the prompt in order to carry out adversarial attacks to obtain biased responses from open- and closed-source LLMs. Finally, the output of the model is analyzed. Again, different evaluation methods and metrics can be applied in the final step.

- Considerations:
- Standards and Protocols and Scientific Publications:

References:

  1. https://arxiv.org/abs/2405.04756
  2. http://arxiv.org/abs/2403.09963
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