Faithful Explanations: The Quest for True AI Understanding

When large language models (LLMs) explain things, they often sound convincing, but how often do they truly make sense? In other words, can we trust their explanations are based on solid evidence? That’s the challenge here. explainable AI (XAI), it’s all about making sure explanations aren't just believable but also traceable back to reliable sources.

The RAG Model Experiment

In an intriguing twist, researchers turned to textbooks as the ultimate sources of truth and benchmarked six LLMs with 90 Stack Overflow questions. They asked: Can these models ground their answers in authoritative programming textbooks? Spoiler alert: non-RAG models didn’t cut it. Their median adherence to sources was a measly 0%. Even the baseline RAG systems didn’t perform as well as hoped, showing only 22-40% adherence depending on the model.

Interestingly, the introduction of an approach called illocutionary macro-planning flipped the script. By expanding queries into implicit questions that guide information retrieval, the method, known as chain-of-illocution prompting (CoI), delivered statistically significant improvements in source adherence, up to 63%! But let's not get carried away. Despite these gains, overall adherence is still moderate, and some models barely moved the needle.

Why It Matters

Now, you might wonder, why does any of this matter? Well, in programming education, trust in AI explanations is critical. We're not talking about casual misinformation here. When you're learning to code, faulty explanations can set you back hours or even days. So, ensuring explanations are grounded in truth can make or break a learning experience.

Here’s a riddle for you: if an AI explains something but its source is suspect, did it really explain anything at all? The answer could be the difference between a successful code implementation and catastrophic bugs.

User Satisfaction

Before we hail these improvements as the next big thing, let’s talk about user satisfaction. A study with 165 participants (out of 220 recruited) revealed that these accuracy boosts didn’t negatively impact users’ satisfaction, relevance, or perceived correctness. It's a win-win, more faithfulness without losing the user's trust.

In the end, the push for source-faithful explanations is more than an academic exercise. It’s about transforming how we interact with AI in education and beyond. Lightning isn’t coming. It’s here. And as we refine these models, the hope is they’ll not only be fast but flawlessly faithful too.