Towards Transparent AI: A Survey on Explainable Large Language Models

LLMs currently function as "black boxes" that cannot easily explain their decision-making processes, which is a major barrier to using these powerful AI systems in high-stakes applications like healthcare, law, and finance where understanding why an AI made a decision is critical.

Researchers have developed multiple explainability techniques tailored to different types of AI architectures (encoder-only, decoder-only, and encoder-decoder models), suggesting that there is no one-size-fits-all solution to making LLMs transparent—different approaches work better for different AI system designs.

The field still lacks standardized methods for evaluating whether explanations are actually good and trustworthy, which means we cannot yet reliably measure whether the explanations LLMs provide are truly helping humans understand the AI's reasoning or just appearing to do so.

Explainability techniques are being applied to real-world problems, indicating that practical solutions exist today, though the survey suggests these applications remain limited and more work is needed to scale transparent AI across industries.

Developing transparent and responsible LLMs requires ongoing research across multiple fronts—from improving explanation methods to better evaluation standards—highlighting that explainability is an active challenge rather than a solved problem in AI development.