Don't think of LLMs as entities but as simulators. For example, when exploring a topic, don't ask:
"What do you think about xyz"?
There is no "you". Next time try:
"What would be a good group of people to explore xyz? What would they say?"
The LLM can channel/simulate many perspectives but it hasn't "thought about" xyz for a while and over time and formed its own opinions in the way we're used to. If you force it via the use of "you", it will give you something by adopting a personality embedding vector implied by the statistics of its finetuning data and then simulate that. It's fine to do, but there is a lot less mystique to it than I find people naively attribute to "asking an AI".
LLMs are injective and invertible.
In our new paper, we show that different prompts always map to different embeddings, and this property can be used to recover input tokens from individual embeddings in latent space.
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Hey @GoogleDeepMind please give us Deep Research with Gemini 2.0 instead of 1.5 , and show inference time compute (thinking/reasoning) outputs 🙏 please also give access to this amazing service via API!
If the dumb parrot (ChatGPT) only knew that for all s:
reverse(reverse(s)) = s
It would not have reversed the list twice. I guess symbolic logic does count, after all 🙂
How & where do large language models (LLMs) like GPT store knowledge? Can we surgically write *new* facts into them, just like we write records into databases?
Explainer 🧵 on how interpretability & model editing go hand-in-hand, and why these emerging areas are so important 👇