I'm curious, how explainable are the decision-making processes of LLMs? For example, is it possible to understand why a model prefers a particular response? While open-source alternatives might be a bit more transparent, what about closed systems? What's the general approach to this issue?
How transparent are AI models?
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Well, I think it makes sense to compare this topic with web browsers. For example, in Chrome, we can look at a page's source code, but we can't see the core processes—we can only infer what's happening during the rendering process. With LLMs, it's somewhat similar: in open-source models (like Llama), we can open up the model weights and see which parameters are effective, but with closed models (like ChatGPT), we don’t have that luxury. There, we can only work backward from the output, much like trying to understand the rendering process from Chrome’s source code.
With open-source alternatives (like Mistral or Zephyr), I think we have a bit more transparency—you can even see the model documentation and fine-tuning data. With closed systems, though, it’s close to a "black box" scenario; you’re reverse-engineering based only on API responses. Honestly, I think this comparison clearly shows that as LLMs mature, they might eventually offer a "debug mode" similar to Chrome. Who knows? Maybe in the future, closed models could even explain things like, "In 60% of the responses I gave, factor X was the dominant influence."