2 ms·
This is cool but I feel like you lose the best part of language-diffusion models which is their ability to edit early tokens.
by mountainriver 2y ago
This is cool but I feel like you lose the best part of language-diffusion models which is their ability to edit early tokens.
- drak0n1c 2y agoThose early tokens aren't necessarily immutable, they still could be "edited" depending on UI. Human conversation and even internal compositional cogitation is full of "what I meant by that" or "on second thought" type clarifications and corrections. Sometimes these aren't verbosely disclaimed, there's body language involved. Likewise there could be occasional lookback parsing and later blocks could convey modifications. The UI can then highlight those revisions transparently by applying strikethrough styling, coloration, dotted underline with tooltip on hover, etc. Like we've seen with human interactions and media, this may be susceptible to misinterpretation by the reader or listener, especially via second-hand clips or screenshots lacking full context. But if the UX is clean and speedy it would be less likely.
- magicalhippo 2y agoI'm reminded of the Physics of Language Models[1] where they showed a standatd autoregressive LLM got a lot more accurate if the models got access to the backspace key, so to speak. [1]: https://physics.allen-zhu.com/home https://physics.allen-zhu.com/home
- deleted 2y ago[deleted]
- sailingparrot 2y agoTo be fair, it's not "obviously" better, but it opens a new point on the tradeoff curve. For a lot of use cases full autoregression is clearly better, and for some others ful diffusion will still be better. Autoregressivity has high quality outputs but is fairly slow. Diffusion has low quality output but is quite fast. This allows you to go in the middle, not as high quality as full autoregression and not as fast as full diffusion, but a balance between both.