Knowledge Graphs and LLM: AI-powered QA Capabilities

Large Language Models have completely revolutionized how we search and digest information online. Large Language Models can produce fairly accurate results by analyzing and aggregating information from various sources. However, these capabilities come with a few limitations, the most notable of which is the inability of LLMs to answer questions accurately when presented with documents containing contradictory information.

In such cases, most LLMs simply answer questions based on a single line of text, significantly reducing their accuracy in question-answering tasks. Knowledge graphs, on the other hand, can effectively organize and represent structured information, facilitating efficient data retrieval and inference. Unfortunately, they lack the capability to represent information in an engaging format that resonates with the user’s intent.

When combined, Large Language Models and knowledge graphs can present an excellent opportunity to fully leverage and improve the capabilities of AI systems, particularly when it comes to question answering.

This article will delve into the intricate relationship between Large Language models and knowledge graphs, with a keen focus on their individual strengths and weaknesses and how you can overcome them by combining the two technologies.

What is a Large Language Model (LLM)?

A large language model [1] is a type of machine learning model that heavily relies on natural language processing to perform various tasks. Large Language Models are pre-trained on massive datasets to understand and generate human-like text.

The real power of LLMs lies in their deep learning architecture, which is typically based on transformer models. Transformer models excel in interpreting and managing sequential data, making them incredibly effective in understanding context and nuances in language.

This enables applications in text summarization, content creation, chatbots, language translation, and technical assistance.

Types of Large Language Models

Large Language Models come in three types, each denoted by the way it captures and generates information. As transformer-based models, LLMs leverage attention mechanisms to process text by use of encoder-decoder modules.

The primary purpose of the encoder is to process input text and produce numerical representations called embeddings. These embeddings can then be used to capture the context and meaning of text.

The decoder, on the other hand, takes these embeddings as input and analyzes them to generate relevant, meaningful sequences of text.

Based on the underlying architectural structures of their transformer models, LLMs can be divided into the following categories:

• Encoder-Only LLMs

Encoder-only Large Language Models only utilize the encoder to process sequential text input in order to understand the contextual relationship between words in a sentence. As such, they’re only suitable for performing tasks that require the interpretation of individual words within a complete sentence.

This gives Encoder-only LLMs unmatched capabilities in tasks such as sentiment analysis, named entity recognition, and text classification.

• Decoder-Only LLMs

As the name suggests, Decoder-only LLMs only use the decoder module to generate output in human-like language. These models are typically trained to predict the next word in a sentence based on the previous context, thus enabling them to produce a relevant, coherent output.

Decoder-only Large Language Models are typically used in downstream tasks like machine translation, text generation, and image captioning.

• Encoder-Decoder LLMs

Encoder-Decoder LLMs combine the strengths of both modules. Generally, the input text is encoded for context and then decoded to generate a relevant output.

This enables them to perform more intricate tasks like question-answering and text summarization.

Strengths and weaknesses of Large Language Models

Large Language Models are some of the greatest developments in AI over the past decade. They possess numerous strengths that highlight their impressive capabilities. However, despite their capabilities, they also have a few weaknesses that impact their effectiveness in various tasks. Some of the most notable strengths and weaknesses of LLMs include:

Strengths

• Generalizability: Large Language Models are trained on massive, diverse datasets. By analyzing information contained in various datasets, they can effectively generalize well to different domains, writing styles, and topics.
• Language processing: By interpreting and analyzing semantic and syntactic sentence structures, LLMs can effectively perform various language processing, adaptation, and generalization applications, including natural language understanding, text classification, sentiment analysis, and information extraction tasks.
• General knowledge: LLMs are invaluable resources for accessing general language. They have a vast wealth of information from the massive datasets they’re trained on. As such, they can assist in research and various other applications, including knowledge expansion, question answering, information synthesis, and much more.

Weaknesses

• Hallucination: Hallucination is one of the greatest challenges facing the efficient utilization of Large Language Models. Hallucination generally occurs when a model over-generates or makes assumptions based on inaccurate or incomplete data, resulting in factually incorrect information.
• Indecisiveness: LLMs heavily rely on probability models for reasoning. The indecisive nature of probability models can drastically impact the ability of LLMs to make decisive choices, especially when fed with contradictory or ambiguous inputs. As such, LLMs have a high probability of producing inconsistent or uncertain responses, thus affecting the reliability and coherence of their output.
• Black-Box Nature: LLMs are often considered ‘black boxes’ due to the difficulties in understanding their internal workings. This lack of interpretability can raise concerns about the models’ trust, accountability, and potential for biased information.
• Lack of Domain-Specific Knowledge: LLMs are fairly good at processing broad general language. However, they struggle with domain-specific knowledge and up-to-date information, which may result in outdated or incomplete outputs when faced with rapidly evolving fields.
• Implicit Knowledge: LLMs typically rely on the implicit knowledge [2] present in their training data to generate an output. Unfortunately, this data is sourced from diverse sources, some of which may contain biased or inaccurate information. By analyzing already trained data, LLMs may produce biased or inaccurate outputs representative of their training data.

Read more: LLM Implementation Strategy: Preparation Guide for Using LLMs

What is a knowledge graph?

A knowledge graph is a data structure that represents information in a network of interlinked entities. The technology can trace its roots to previous research on graph theory and knowledge representation. Their capability to organize and represent structured information in a machine-readable format makes them effective tools for capturing and connecting entities, their relationships, and their attributes.

Additionally, by leveraging rich data connections to empower advanced reasoning, knowledge-based applications, and semantic research, knowledge graphs facilitate deeper understanding and utilization of information in various domains.

Types of Knowledge Graphs

Knowledge graphs (KGs) can be grouped into four categories, each of which captures different facets of knowledge and serves a specific purpose. These categories include:

• Common-Sense Knowledge Graphs 

  As the name suggests, common-sense knowledge graphs focus primarily on capturing everyday, intuitive knowledge about the world. By gaining the implicit knowledge that humans possess, KGs enable machines to make inferences based on common-sense understanding.

• Encyclopedic Knowledge Graphs 

  These types of KGs are tied to specific domains or industries. As such, they only capture and organize structured information relevant to a particular field, such as finance or healthcare, thus facilitating the creation of a more specialized knowledge representation.

• Multimodal Knowledge Graphs 

  Multimodality is a representation of data using information sourced from different entities with multiple representations. [3] As such, multimodal knowledge graphs capture and integrate information from different modalities, including text, audio, video, and images. By capturing data from diverse sources of information, multimodal KGs can capture a more comprehensive understanding of data, enabling them to facilitate tasks like image-text matching, multimodal search, and recommendations.

Strengths and weaknesses of knowledge graphs

Knowledge graphs have various strengths that make them uniquely capable tools for performing various functions. However, they’re also limited by some weaknesses that may limit their capabilities. Some of the most notable strengths and weaknesses of KGs include:

Strengths

• Decisiveness: By providing implicit and well-defined relationships between entities, KGs can effectively aid in making decisive choices. This enables machines and other AI applications to reason and infer new knowledge based on the available information, thus supporting a more effective, informed decision-making process.
• Structural Knowledge Representation: KGs facilitate effective information organization, querying, and navigation. By providing a structured framework for representing interconnected knowledge, they allow users to easily explore and understand complex data connections.
• Interpretability and Explainability: KGs are specially designed to facilitate the seamless interpretation of data by humans. This explicit representation of relationships and entities allows for an easy and transparent understanding of the data and the reasoning behind the connections, thus enhancing interpretability and making it easier to identify any errors and biases within the data.
• Domain-Specific Knowledge Capture: KGs, particularly domain-specific knowledge graphs, can be tailored to capture domain-specific relationships and information. This unique approach enables more accurate and focused insights, analysis, and applications in specialized areas such as finance, healthcare, and scientific research.
• Evolving Knowledge: Unlike LLMs, which are limited to the scope of information contained in their training data, KGS can evolve and adapt to incorporate new updates and information. Additionally, these graphs can be modified or expanded as new data sources become available. This way, KGs can provide relevant, up-to-date information.

Weaknesses

• Incompleteness: Like with most knowledge repositories, knowledge graphs are limited to the information available at the time of their creation. There’s also the possibility that they may not capture the entire knowledge contained in the domain, thus increasing the chance of gaps and missing data. In such cases, users may face limitations in understanding and decision-making when they encounter unprecedented relationships and entities in the information provided.
• Lack of knowledge and understanding: Despite their impressive capabilities in capturing structured data, knowledge graphs lack the ability to understand and process natural language as well as deal with unstructured text. As such, they cannot interpret context, nuances, and semantics conveyed through text.
• Unseen Facts and Updates: KGs have to be constantly updated with new information to provide up-to-date information. Failure to do this can result in potentially outdated knowledge, thus hindering the graph’s dependability.

Read our case study: LLM-based Assistance Bot to enhance airport operations

Knowledge graphs & LLMs

Large Language Models and model graphs have unique strengths and weaknesses. In some cases, the strength of a particular technology may help overcome the limitations of the other. As such, combining their capabilities may help address their potential limitations and enhance their effectiveness.

Here are a few examples of how KGs and LLMs can be unified to create more robust AI systems:

Retrieval Augmented Generation (RAG)

Large Language Models can significantly simplify information retrieval from knowledge graphs. They do this by providing user-friendly access to complex data, thus eliminating the need to search databases through traditional programming languages.

Knowledge graphs can also be combined with LLMs for knowledge-intensive NLP tasks through a process called retrieval augmented generation (RAG). In RAG, the LLM first retrieves the relevant information from the knowledge graph using semantic and vector searches. The LLM then augments the response with the contextual data contained in the knowledge graph. [4]

When properly leveraged, this LLM-knowledge graph utilization can generate more precise, contextually relevant, and accurate information while effectively reducing the possibility of model hallucination.

Knowledge Graph-enhanced Large Language Models

Large Language Models need a vast amount of training data to be accurate and effective. Considering this fact, knowledge graphs can serve as a reliable source of training data. This may involve incorporating the knowledge graph into the LLM during the pre-training stage, thus allowing the model to learn directly from the graph.

Another possible enhancement is integrating the knowledge graph into the LLM during the inference stage. Alternatively, knowledge graphs can be used to interpret the facts and reasoning process of LLMs, thus enhancing their interpretability.

Final thoughts

Both Large Language Models and Knowledge graphs are complex, state-of-the-art technologies with unique strengths and weaknesses. While combining them may prove challenging, effectively leveraging the two technologies combined can pave the way for potentially more robust AI systems.

The reasoning behind this is pretty straightforward: by leveraging their unique strengths to overcome the limitations of the other, users can effectively analyze vast amounts of data and get accurate, verifiable outputs.

References

[1] Techtarget.com. Large Language Models (LLMs).
URL: https://www.techtarget.com/whatis/definition/large-language-model-LLM. Accessed on March 11, 2024
[2] Trainingindustry.com. Implicit Knowledge. URL: https://tiny.pl/dgstb. Accessed on March 11, 2024
[3] Sciencedirect.com, Multimodality. URL: https://tiny.pl/dgs9w. Accessed on March 11, 2024
[4] Nvidia.com, What Is Retrieval-Augmented Generation, aka RAG? URL: https://blogs.nvidia.com/blog/what-is-retrieval-augmented-generation/. Accessed on March 11, 2024