[tkgally]

What I’ve sometimes done when trying to make sense of recent LLM research is give the paper and related documents to ChatGPT, Claude, or Gemini and ask them to explain the specific terms I don’t understand. If I don’t understand their explanations or want to know more, I ask follow-ups. Doing this in voice mode works better for me than text chat does.

When I just want a full summary without necessarily understanding all the details, I have an audio overview made on NotebookLM and listen to the podcast while I’m exercising or cleaning. I did that a few days ago with the recent Anthropic paper on persona vectors, and it worked great.

[tshannon]

So probably another stupid question, but how do you know what it's spitting out is accurate?

[tkgally]

One has to be aware of the possibility of hallucinations, of course. But I have not encountered any hallucinations in these sorts of interactions with the current leading models. Questions like "what does 'embedding space' mean in the abstract of this paper?" yield answers that, in my experience, make sense in the context and check out when compared with other sources. I would be more cautious if I were using smaller models or if I were asking questions about obscure information without supporting context.

Also, most of my questions are not about specific facts but about higher-level concepts. For ML-related topics, at least, the responses check out.