Battling LLM Hallucinations in Biomedicine: The Role of Knowledge Graphs in Knowledge Injection Techniques

Large Language Models (LLMs) represent a groundbreaking advancement in information processing, reshaping how we access and utilize vast amounts of data. Despite their proficiency in generating text, LLMs occasionally introduce inaccuracies or false information, posing a considerable risk to the reliability and trustworthiness of information, particularly in high-stakes domains like biomedical research.

Enter Knowledge Graphs, the structured repositories that interlink information in intricate webs of understanding. Within biomedicine, these invaluable resources serve as pillars, offering comprehensive insights into the complex relationships among genes, diseases, drugs, clinical trials, and more. They not only capture these intricate connections but also serve as a beacon of accuracy in an ocean of data. ​In this context, injecting clarity into LLMs through Knowledge Injection becomes increasingly important.  

In this article, we will delve into the process of knowledge injection and how it leverages knowledge graphs to fortify the reliability of LLM-generated text. We will also discuss how this innovative approach not only mitigates hallucination but also establishes a pathway toward unwavering accuracy in the realm of biomedical research.

How Do Hallucinating LLMs Affect Domain-Specific Industries Like Biomedicine?

In industries reliant on precision and fact-based communication, such as biomedicine, the consequences of hallucinating assertions from LLMS are particularly severe. The need for reliable, controllable text generation at scale is a foundational requirement that affects critical decision-making, patient care, and research advancements. 

The wealth of biomedical data, spanning from molecular interactions to clinical trial outcomes and beyond, is profoundly intricate. This complexity demands a level of precision and accuracy that surpasses the capabilities of standard language models.

Example of GPT 3.5 Hallucination (Source:Med-HALT)

In biomedicine, where accuracy can be a matter of life and death, the stakes of inaccuracies are significantly amplified. Hallucinating LLMs risk more than just spreading misinformation. They can endanger patients, misguide clinical decisions, and skew research outcomes. A single misinterpreted or falsely generated piece of information could lead to misguided treatment paths or flawed scientific conclusions.

As such, the reliance on accurate and hallucination-free text generation is vital for safeguarding the integrity and safety of biomedical research and healthcare practices.

Causes of LLM Hallucinations in Biomedicine

Navigating the complexities of biomedical information within LLMs presents complex challenges, often leading to hallucinations. These challenges stem from several key factors inherent in the domain:

  • Complexity and Contextual Ambiguity:

Biomedical knowledge is inherently intricate, often characterized by multifaceted relationships and nuanced context. Consider, for instance, the relationship between specific genetic mutations and drug responses. A single gene mutation might interact differently with various medications, producing varying outcomes based on individual patient characteristics. This nuanced interplay is challenging for standard language models to interpret accurately, leading to potential inaccuracies or oversimplifications in generated content.

  • Vast Disparate Medical Information:

The expanse of biomedical data encompasses diverse sources, from published studies to patient records and ongoing clinical trials. For instance, a language model trained on existing clinical data might not have access to the latest findings from ongoing trials. This absence of up-to-date information can hinder the model’s ability to generate contextually relevant responses, potentially leading to outdated or incomplete insights.

  • Incomplete Knowledge Incorporation:

Attempting to encode all available medical knowledge into an LLM’s parameters for comprehensive understanding is daunting. Imagine attempting to capture the entire depth of medical literature within a model’s parameters. It can be difficult to fine-tune a model to discern the context-dependent use of medical terminologies or understand the evolving nature of medical practices over time. As a result, the LLM might generate content that lacks the nuanced understanding necessary in biomedical contexts, contributing to potential hallucinations or inaccuracies.

Implications of Hallucinating LLMs in Biomedicine

Hallucinating LLMs in biomedicine have profound implications beyond the surface-level data inaccuracies. These implications reverberate through critical aspects of healthcare, some of which include:

  • Misdiagnosing of Diseases:

A hallucinating LLM can misinterpret a patient’s symptoms, leading to a diagnosis based on inaccurate information. This could result in delayed treatment or inappropriate management of a medical condition, potentially impacting patient outcomes and well-being.

  • Inappropriate Treatment Recommendations:

An LLM suggesting incorrect treatments based on hallucinated data could lead healthcare providers to prescribe medications or therapies unsuitable for a patient’s actual condition. This can not only jeopardize the individual’s health but can also potentially hamper their recovery process.

  • Misleading Research Findings: 

When LLMs generate inaccurate information, it can misguide researchers, altering the trajectory of studies. Developing hypotheses or research directions based on misguided data can result in skewed conclusions impacting future biomedical investigations.

  • Skewed Drug Discovery Process:

Inaccurate information generated by LLMs during drug development phases could mislead researchers in evaluating drug efficacy or safety profiles. This could potentially result in the advancement of less effective medications or have unforeseen adverse effects, impacting patient treatment outcomes.

  • Ethical and Legal Complications:

Reliance on unreliable content from LLMs might lead to ethical quandaries and legal challenges. Clinical decisions influenced by erroneous data can raise questions about professional responsibilities and patient rights. This can lead to legal repercussions or ethical dilemmas for healthcare practitioners and institutions.

How can Knowledge Graphs Inject Clarity Into Hallucinating LLMs?

Knowledge graphs are dynamic data repositories, interconnecting vast amounts of information in the form of entities and relationships. These graphs offer a visual means to organize and furnish fact-based, contextually rich insights across various domains.  

When integrated with large language models, knowledge graphs offer a means to counter hallucinations through a technique known as Knowledge Injection (KI).

KI involves infusing accurate and contextually appropriate information into LLM-generated content. This infusion offers meticulously curated data from knowledge graphs to LLMs, mitigating the risks of hallucinations and elevating the precision and reliability of the model’s outputs.

Knowledge Injection to LLMs and Its Components

Knowledge Injection (KI) is a technique that leverages the power of knowledge graphs to enhance large language models’ text-generation capabilities. It involves mapping contextually relevant knowledge entities directly from a knowledge graph to an LLM’s prompt, empowering it to generate more accurate and controlled responses for specialized tasks.

Sample illustration of common knowledge injection. The injection process initially matches entities mentioned in the input text (as “mentions m”) with their corresponding entities in an external knowledge base (as “entity ke”). Subsequently, it retrieves the pertinent external knowledge associated with the identified entities. Finally, the injection model combines the input text with the retrieved knowledge, injecting both into the LLM prompt. (Source: Revisiting the Knowledge Injection Frameworks )

For instance, consider an LLM attempting to explain a rare medical disorder. Without contextual guidance, the model might produce a generic or inaccurate explanation. However, by integrating a biomedical knowledge graph into the prompt, specific information about the disorder is introduced. This context enrichment enables the LLM to generate a more precise and informed explanation, drawing from the structured knowledge within the graph.

The process of knowledge injection comprises several critical elements that collectively contribute to improving the reliability of LLMs:

  • Knowledge Graph: 

At the core of KI lies the knowledge graph, housing a comprehensive network of relevant entities and their interconnected relationships. This repository forms the bedrock for contextual understanding, providing a structured framework to derive precise information.

  • Controllable Text Generation (CTG):

LLMs are trained on expansive unsupervised data, often lacking control over specific attributes of the generated text, such as topic, style, or sentiment. This is where the KI technique can leverage controllable text generation to derive constraints from structured knowledge sources, like knowledge graphs or labeled datasets.

Controllable Text Generation (CTG) is an iterative learning process that harnesses the knowledge graph to generate precise sample text fields for LLMs. In applications where precise control over the output is crucial,  CTG ensures that the generated text aligns with predefined attributes. In biomedicine, for instance, CTG can enforce accurate terminology, compliance with healthcare standards, or coherence with verified clinical knowledge. This control allows for the generation of content tailored to specific criteria.

  • Graph-to-Text Mapping:

The knowledge mapping component of the injection process involves translating graph data to text fields for prompts. It orchestrates the assembly of a template prompt infused with rich contextual depth. This graph-to-text mapping helps create detailed and precise prompts by drawing on the graph’s nuanced insights. The resulting template prompt accurately mirrors the intricate relationships found within the graph.

  • LLM Prompt Formatting:

Lastly, the template prompt is strategically inserted into the text space of the LLM. The formatted prompt is enriched with context to help the LLM navigate away from hallucination or guesswork. Instead, the LLM now leverages graph-based context to generate responses that are more reliable, accurate, and of higher quality.

Biomedical Text Generation Using Knowledge Injection Technique

Take the example of a medical professional seeking information about drug interactions using the query:

Example Query: “What are some recent studies for MOUD interactions with other drugs?

In this scenario, employing the KI technique enhances the precision of the query and subsequently influences the response generated by the LLM.

By integrating a biomedical knowledge graph, specific insights from recent clinical trials and ongoing research are injected into the response. This allows for the incorporation of nuanced drug interactions and their effects on patient outcomes, providing comprehensive and accurate information. The knowledge-injected query can look something like this:

Knowledge-Injected Query: “What recent clinical trials or research findings explore the interactions between Medications for Opioid Use Disorder (MOUD) and other drugs?

The knowledge-injected query emphasizes specific biomedical terminology (“Medications for Opioid Use Disorder”) and focuses on exploring recent clinical trials or research findings related to MOUD interactions with other drugs. This refinement ensures a more targeted and precise query, leveraging the insights from a biomedical knowledge graph to guide the inquiry. 

Here is a sample of an enhanced response to the given query from a knowledge-injected LLM powered by Wisecube’s biomedical knowledge graph, Orpheus:

Using Orpheus, the KI technique sets a targeted context for LLMs to draw from verified and detailed biomedical data, fostering a more informed and detailed response that aligns with the latest research, clinical trials, and verified information within the biomedical domain. 

On the other hand, here is how ChatGPT 3.5 responded to the same query:

The lack of specificity in ChatGPT’s response clearly hints at the absence of a dedicated biomedical knowledge graph that could provide crucial context. Consequently, the LLM’s reply to the query lacks detailed insights and references to recent studies, compromising its specificity and depth of information.

Leveraging Wisecube Orpheus Knowledge Graph for Hallucination-Free Biomedical Research

Wisecube’s Biomedical Knowledge Graph, Orpheus, stands at the forefront as the largest repository of validated biomedical knowledge, housing billions of facts concerning millions of biomedical entities. This monumental achievement is realized through the integration of data sourced from thousands of outlets, meticulously woven together using our cutting-edge AI technology. 

Orpheus operates as a trusted source, boasting a comprehensive network of interconnected entities and relationships within the biomedical realm. Leveraging the prowess of Natural Language Processing (NLP), Orpheus facilitates advanced relationship inference, enabling sophisticated comprehension and extraction of intricate connections among biomedical concepts.

As a one-of-a-kind AI-powered biomedical knowledge graph, Orpheus is an invaluable resource for injecting knowledge into LLMs within the biomedical domain. By leveraging Orpheus, LLMs gain access to a wealth of validated and interconnected biomedical data.

When integrated with LLMs, Orpheus can significantly enhance the quality and accuracy of generated biomedical text. By tapping into comprehensive and verified biomedical insights, Orpheus guides LLMs to produce contextually relevant and reliable content. This integration minimizes inaccuracies, fostering informed decision-making and propelling advancements in biomedical research.

Utilizing Orpheus to infuse biomedical knowledge into LLMs unlocks a multitude of valuable applications and benefits across various use cases:

  • Enhancing the quality of automated medical reports
  • Robust clinical decision support
  • Accurate drug interaction predictions
  • Improved patient education and healthcare communication
  • Enhancing the precision of predictive models for safer pharmaceutical practices

If you are ready to elevate the precision of your biomedical text, contact us today to learn more about the potential of Wisecube’s Orpheus for amplifying the reliability of your LLM outputs.

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