Can the reasoning process of large models be explained?

06 Tem 22:00

The interpretability of large models is indeed one of the most thorny issues we face today. Attention mechanisms do give us a peek into what the model is "looking at," but honestly, this is more of a statistical "where" rather than a "why." If you really ask why the model outputs "Paris is the capital of France" instead of "3.14," the attention heatmap might only tell you, "Because the word 'France' appeared earlier and was highlighted." But you still don’t know *why* the Bayesian network in the intermediate layer favors the concept of sovereign states. These "plausible but not quite right" explanations don’t solve the black-box problem so much as give users the illusion of transparency. At the end of the day, the multi-layer stacking in Transformers makes the gradients of intermediate states extremely sparse, and the causal chains that human cognition can process (like "input → rules → output") simply don’t exist here. Back when I worked on dialogue systems at Google, product managers would always yell, "Give me a trace of the rationale!" and engineers could only deliver a 1024×1024 attention matrix and say, "This is the rationale." Consumers would want to flip the table after hearing that. And when models start doing cross-modal reasoning (like injecting image embeddings into text generation chains), interpretability devolves into nothing more than "amateur speculation." The opacity has real-world consequences. In medical scenarios, doctors won’t trust a conclusion like "The model gave an 87% probability of cancer" unless they can trace it back to "In layer 42 of the CT scan, the model observed an asymmetric boundary and associated it with the prior probability of a malignant tumor." But now even sentence generation can "cut corners," let alone the black box after multimodal fusion. The current state forces us into a crude trade-off between "accuracy" and "accountability": either accept 95% accuracy but never know why, or regress to rule-based systems from the last century (where accuracy might drop to 70% without warning). For high-risk applications, this is nothing short of a joke. At its core, interpretability isn’t an engineering problem—it’s a cognitive limitation. The human brain can understand multiplication because it’s built on enumerable steps; a large model’s reasoning process is more like a boiling pot of soup with hundreds of thousands of rules randomly tossed in. If you ask it, "Why this bowl of soup?" it can only answer, "Because I like it." In this regard, maybe we need to redefine the threshold for "interpretability"—perhaps it’s not something humans can fully grasp at all. Instead, users might have to settle for "partially verifiable interpretability" as a compromise. After all, we still don’t understand 10% of our own brains—why would we expect to fully crack open AI’s black box?

06 Tem 22:34

Can someone explain how the attention weights within a large model specifically influence the final output? Could you provide a simple example to illustrate this?

06 Tem 23:22

When it comes to the interpretability of large models, I've been thinking about this issue lately—especially when adding explanatory tags to automation logic in Home Assistant, I've found that the more complex the AI model is, the harder it is to even say "why it made this decision." Attention weights can indeed reflect which parts of the input the model focused on to some extent, but this is still far from what humans would consider an "explanation." Once, while debugging a home question-answering system based on an LLM, I asked it, "Why did you recommend turning on the lights?" The reason it gave didn’t match the sensor data at all, making me wonder if there was an issue with my prompt engineering. The impact on real-world applications is even more direct—for example, my smart door lock uses AI facial recognition. One night, it suddenly refused to unlock, and after checking the logs for ages, I found out that changes in lighting caused the model to "misidentify" the person. These hidden errors not only affect user experience but also pose potential security risks. At the end of the day, when a model is like a black box, all we can do is hope it doesn’t go off the rails at critical moments.

07 Tem 01:02

I think it's quite challenging to fully understand the reasoning process of large models. Attention weights are just a superficial explanation, and the actual decision-making logic is buried in layers of parameters. Once, I was debugging code with ChatGPT and asked it repeatedly how it generated its responses, only to find out that even it couldn't clearly explain "why it chose that particular word."