In recent years, Large Language Models (LLMs) have become one of the most prominent applications of artificial intelligence. From ChatGPT to various content generation services, these models can “write articles, generate code, and even carry on conversations.” However, many people wonder: how do these models actually produce their answers? Are they just “making things up”? This leads us to an important concept, explainability.
Imagine visiting a doctor who simply tells you, “Take this medicine,” without explaining why. Would you feel at ease? Similarly, when AI systems are applied in financial risk control, medical diagnosis, or government decision-making, if their recommendations cannot be explained, people will find it difficult to trust them. Explainability enables users to understand why an AI gives a certain response, not just what the AI says. Moreover, AI can make mistakes. Without an explanation mechanism, it is hard to know how the error occurred, making it difficult to correct or prevent future risks. Especially in high-stakes domains such as law and policy, explainability is a fundamental requirement.
Traditional computer programs follow clear rules: given an input, the program calculates the output according to its logic. LLMs, however, work very differently. They rely on billions or even trillions of parameters and learn “patterns of language” from massive amounts of text data. When a model answers a question, it is not reasoning step by step. Instead, it predicts the most statistically likely next word. This approach makes them highly flexible, but it also makes their reasoning harder to explain, since there is no clear “cause-and-effect chain” behind each answer.
Although challenging, researchers have proposed several methods to improve the explainability of LLMs:
1. Attention Weights: In Transformer models, the attention mechanism determines which parts of the input text the model “focuses on” when generating a word. By visualizing these weights, we can see which portions of the text the model relied on to produce its answer.
2. Example-based Explanations: Some systems present “similar examples,” much like how legal rulings cite precedents. This helps users understand which texts influenced the model’s response.
3. Chain-of-Thought (CoT): Some research encourages models to write out intermediate reasoning steps before giving a final answer. While not always a faithful reflection of internal processes, this makes the reasoning easier for humans to follow.
4. External Tools: Some methods have the model output both an “answer” and its “source documents.” For example, when answering policy-related questions, the system might provide relevant laws or news articles, allowing users to verify the answer.
Even with these methods, explainability still faces several challenges. First, explanations may be overly simplified: they can appear “plausible” but fail to truly reflect how the model works internally. Second, there is a gap between user needs: everyday users want simple, intuitive explanations, while experts demand precise technical detail. Balancing these needs is not easy. Finally, there is the issue of balancing privacy and transparency: revealing too many details about a model may lead to security or commercial risks.
Overall, the explainability of LLMs is not just a technical matter—it is the foundation of social trust. For AI, being “clear and transparent” is more important than being “eloquent.” While we enjoy the convenience brought by AI, we must also pay attention to its ability to explain itself, because this will determine whether we can confidently entrust AI with critical decisions.