How Large Language Models Are Transforming Explainable AI: Enhancing Transparency and Building Trust

In a March 31, 2025 survey titled LLMs for Explainable AI, researchers Ahsan Bilal, David Ebert, and Beiyu Lin explore how large language models can bridge the gap between complex machine learning outputs and human understanding.

Traditional machine learning models often operate as black boxes, hindering user trust and understanding. Large Language Models (LLMs) offer a potential solution by translating complex outputs into natural language narratives, enhancing explainability and transparency. This approach aims to bridge the gap between sophisticated model behavior and human interpretability, fostering trust and accountability in decision-making processes.

In recent years, artificial intelligence (AI) has become increasingly integrated into our daily lives, from personalized recommendations on streaming platforms to diagnostic tools in healthcare. However, a significant barrier to the broader adoption of AI remains: its opacity. Many advanced AI models operate as black boxes, making their decision-making processes opaque to users. This lack of transparency can erode trust and hinder the responsible use of AI. Enter Large Language Models (LLMs), which are emerging as powerful tools to enhance Explainable Artificial Intelligence (XAI) by translating complex decisions into understandable narratives.

The Role of LLMs in Enhancing XAI

Explainable AI, or XAI, refers to methods and techniques designed to make AI systems more transparent and interpretable. Unlike traditional AI models that operate in obscurity, XAI aims to provide insights into how these systems arrive at their decisions. This is crucial for building trust, ensuring accountability, and enabling users to make informed decisions based on AI outputs.

LLMs play a pivotal role in this transformation by leveraging natural language processing (NLP) capabilities. These models can generate human-readable explanations of complex AI decisions, bridging the gap between technical jargon and user understanding. For instance, an AI model used in healthcare might flag a lung scan as abnormal. With the help of an LLM, it can provide a detailed explanation, highlighting specific patterns associated with certain diseases, thereby aiding doctors in making informed treatment recommendations.

Deepening Understanding: Methods and Challenges

The integration of LLMs into XAI involves various methods, including prompting techniques that guide the model to generate clear explanations. Researchers have demonstrated how simple prompts can identify key features influencing an AI’s prediction and create counterfactual scenarios, enhancing transparency. For example, in finance, an AI system predicting market trends could use an LLM to explain its reasoning, helping analysts understand and trust the predictions.

However, this approach is not without challenges. Ensuring that explanations are both accurate and comprehensible requires balancing technical precision with simplicity. Additionally, there are concerns about potential biases within LLMs themselves, which can affect the quality of explanations provided. Addressing these issues is essential to fully harness the potential of LLMs in XAI.

The Importance of Trust in AI Systems

Trust is a cornerstone of any successful technology adoption, and AI is no exception. In critical domains like healthcare and finance, where decisions can have life-altering consequences, transparency is paramount. Users need to understand not just what an AI system decides but also why it makes that decision.

LLMs contribute significantly to this trust-building process by providing clear, natural language explanations. For instance, in personalized patient care, an LLM-powered AI can explain the rationale behind treatment recommendations, empowering both doctors and patients to engage more effectively with the technology. Similarly, in finance, transparent explanations from AI systems can enhance decision-making processes, fostering confidence among stakeholders.

Conclusion: The Future of Transparent AI

The integration of Large Language Models into Explainable AI represents a significant step forward in making complex AI systems more accessible and trustworthy. By translating intricate decision-making processes into understandable narratives, LLMs are paving the way for broader adoption of AI across various sectors.

As we continue to refine these technologies, addressing challenges such as bias and ensuring robust evaluation metrics will be crucial. The potential benefits of transparent AI are immense, offering not just improved decision-making but also fostering a culture of accountability and responsibility in AI development and deployment.

In conclusion, the synergy between LLMs and XAI is transforming how we interact with AI systems, promising a future where technology is not only powerful but also transparent and trustworthy.