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σ-GPTs: A new approach to autoregressive models
- hammock 2y agoAdd to this some kind of "autofocus" for the user to click on the word that is the "center" of the prompt and you've really got something
- skilled 2y ago> The main idea is to train the model to generate sequences in a random order, which allow conditional density estimation, infilling and generating sequences by burst using a novel rejection sampling method. > In exploring that idea, we aslo compared to a discrete diffusion baseline, which also allows to generate sequences in burst. We were surprised to see that diffusion models were able to solve path-finding task and we made a short Twitter thread The said thread: https://nitter.poast.org/ArnaudPannatier/status/1762864347397628396 https://nitter.poast.org/ArnaudPannatier/status/176286434739... And a showcase here: https://www.idiap.ch/~apannatier/sigma-gpt/ https://www.idiap.ch/~apannatier/sigma-gpt/ (excerpt taken from here: https://www.idiap.ch/~apannatier/ https://www.idiap.ch/~apannatier/)
- deleted 2y ago[deleted]
- 3abiton 2y agoI just wonder if such models based on their method would make hallucination even worse.
- arnaudpannatier 2y agoHey, I'm Arnaud, first author of the paper. The answer is a bit mixed. We actually started looking into this because of a repetition problem that appeared in a low-data regime for a sequence generation task. Basically, the left-to-right GPT was stuck repeating the same token once it sampled twice the same in a row during generation. And to mitigate that, we tried to generate the sequence in a random order and it seemed to help and we see less of this repetition issue. We initially thought when we don't have enough data, shuffling would be like data-augmentation and might actually help the model reach better performance. But this is not what we found in the experiments: apparently as learning in any order is a harder task, the model memorise the data more.
- nico 2y ago> We were surprised to see that diffusion models were able to solve path-finding task I wonder if this type of method might allow for faster solutions to the traveling salesman problem
- cs702 2y agoThis looks great. The authors randomly permute (i.e., shuffle) input tokens in training and add two positional encodings to each token: one with the token's position and another with the position of the token to be predicted. Otherwise, the model is a standard autoregressive GPT. The consequences of this seemingly "simple" modification are significant: * The authors can prompt the trained model with part of a sequence and then decode the missing tokens, all at once, in parallel, regardless of order -- i.e., the model can in-fill in parallel. * The authors can compute conditional probability densities for every missing token in a sequence, again in parallel, i.e., densities for all missing tokens at once. * The authors propose a rejection-sampling method for generating in-fill tokens, again in parallel. Their method seems to work well in practice. I've added this to my reading list. Thank you for sharing it on HN.
- thomashop 2y agoI don't understand how that parallel prediction can work... Let's say I give it as input the sentence: I . . . . . . . . happily. The second word to be predicted depends on the first word.
- cs702 2y agoGive the model the tokens "happily" and "I", and add to each input token its respective position embedding and the position embedding for the token to be predicted. You can do this in parallel for all tokens to be predicted. The model has been trained so it can predict tokens in any position.
- hexomancer 2y agoYes, but is there any guarantee that the complete sentence makes sense?
- entropicdrifter 2y agoThat guarantee didn't exist with regular GPT LLMs, did it? It just came about as an emergent property of throwing more and more compute, training data, and training time at the problem
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- klysm 2y agoEncoding the sequence like that seems like a really clever workaround for some of the data dependency limitations of GPT.
- szvsw 2y agoWow, really cool concept! I wonder if this starts to become similar dynamics to what we see in image generation models, where structure/detail emerges in one region of the image and then the surrounding areas start to resolve themselves into place. That kind of behavior seems particularly useful for longer reasoning/logic/planning, where the big ideas might become apparent first, and then the interstitial details and text just naturally fill in…
- byteknight 2y agoThe process you describe is referred to as diffusion
- szvsw 2y agoYep yep I know, but I was trying to suggest something diffusion-like occurring with a language model through a totally separate mechanism that does not rely on the denoising process (at least not literally).
- byteknight 2y agoIt kinda does that but one token at a time.
- immibis 2y agoI'm fairly certain diffusion refers to the overall architecture, not the emergent self-organization process.
- byteknight 2y agoIt refers to the process of taking noise and "diffusing" it until visually appealing
- bigyikes 2y agoIs this applying the learnings from vision transformers to language transformers? If I understand correctly, vision models split an image into tiles and append a positional encoding to each so the model can understand the relative position of each tile. I admittedly only read the abstract - a lot of this stuff goes over my head - but it seems like this paper proposes a similar idea, but for 1D instead of 2D?
- seurimas 2y agoPositional encoding is standard for transformers of all stripes. They introduce a seemingly novel, redundant positional encoding scheme. It's more difficult to train, but seems to enable producing multiple tokens at once (i.e. you could get an answer that is N tokens long in N/x steps instead N steps).
- cwilkes 2y ago[flagged]
- optimalsolver 2y agoYann LeCun would say [0] that it's autoregression itself that's the problem, and ML of this type will never bring us anywhere near AGI. At the very least you can't solve the hallucination problem while still in the autoregression paradigm. [0] https://twitter.com/ylecun/status/1640122342570336267 https://twitter.com/ylecun/status/1640122342570336267
- vessenes 2y agoI think this method might not be amenable to the exponential divergence argument actually. Depending on token sampling methods, this one could look at a proposed generation as a whole and revise it. I’m not sure the current token sampling method they propose does this right now, but I think it’s possible with the information they get out of the probabilities.
- modeless 2y agoYes, to me this seems to address LeCun's objection, or at least point the way to something that does. It seems possible to modify this into something that can identify and correct its own mistakes during the sampling process.
- vessenes 2y agoWell, I think I understand LeCun has a broader critique that any sort of generated-in-a-vacuum text which doesn't interact with meatspace is fundamentally going to be prone toward divergence. Which, I might agree with, but is also, just, like, his opinion, man. Or put less colloquially, that's a philosophical stance sitting next to the math argument for divergence. I do think this setup can answer (much of) the math argument.
- _w1tm 2y agoDoes everything have to take us towards AGI? If someone makes a LLM that’s faster (cheaper) to run then that has value. I don’t think we want AGI for most tasks unless the intent is to produce suffering in sentient beings.
- behnamoh 2y agoTitle is incorrect: it's σ not Σ.
- modeless 2y agoΣ is uppercase σ. Maybe this happened automatically? Pretty funny if so. Correct in a Greek context; clearly incorrect in a math context.
- mehulashah 2y agoYes, HN automatically did that.
- modeless 2y agoFor future reference, it is possible to edit the titles of stories you've submitted. This allows you to correct any errors introduced by HN's title rewriting heuristics at submission time, without waiting for a moderator to do it for you. Just like for comments, though, the edit window is time limited. For comments the window is two hours. I don't know if it's the same for story titles.
- smusamashah 2y agoThere is a video on twitter showing it generating text (looks a bit like image diffusion) https://x.com/ArnaudPannatier/status/1799055129829839166 https://x.com/ArnaudPannatier/status/1799055129829839166
- lukasb 2y agoWeird that they chose an example that ended up somewhat nonsensical.
- sebzim4500 2y agoPart of the issue is they are training a pretty tiny model, it's not like GPT-2 ~100M is especially coherent either.
- mbil 2y agoI wonder if this would help especially for computer code generation, where what is output at a given step may materially depend on what would be written at a later step.
- mbil 2y agoAnd, maybe prohibitively slow, perhaps integrate some kind of linting or syntax checking as part of the rejection sampling. I.e. burst sample N potential generated snippets in parallel, reject those that are syntactically invalid.
- barfbagginus 2y agoIt would be nice if it could diffuse right on the AST. That would ensure each generated item passes a syntax check, without the waste of rejection sampling
- matmulbro 2y ago[dead]
- barfbagginus 2y agoGreat, now I'm imagining GPT flexing its roided biceps while making sigma faces, as edgy incel music goes hard in the background with spiky synths and a boy's choir. After seeing how awesome the showcase looks, I'm not even sure I'm mad about this, lol
- aconz2 2y agoIs there code somewhere? I don't totally understand the double position and shuffling. Interesting they use concat instead of plus for the positionals
- nsagent 2y agoWhat's old [1] is new again... without citing prior work. It's not like it's an unknown work. It was published in ICML and has ~250 citations. [1]: https://arxiv.org/abs/1902.03249 https://arxiv.org/abs/1902.03249
- naveen99 2y agoBert had random masking from the sequence. But time is sequential.
- vessenes 2y agoI kept thinking about this paper today, and I really like the capabilities. A number of things that are relatively hard for sequential LLMs are easy here. Want json? Fix curly brace tokens to the beginning and end. Want a specific token-length explanation of an answer? Write a short answer, post-pend it, and infill. Want a higher-density answer to something? Add a density assessment section to your generated text, a space for the LLM to score info-density, and generate looking for a high density score. I would guess there's a lot here to be experimented with. It would be nice to get an 8b parameter model with reasonable number of tokens (x3 based on the paper, sadly) through it.
- zakkor1 2y ago> Fix curly brace tokens to the beginning Regular LLMs can already do this, by prefilling the start of the assistant's response. But there is actually something even better: you can constrain the LLM's output to a specific grammar (like JSON), so it'll only be able to answer with syntactically valid JSON.
- vessenes 2y agoYes. And you can have a grammar parser only select from valid tokens in a randomized distribution. But, this feels much more sophisticated to me, especially if you can mix specific token-based grammar requirements with other instructions during the token selection phase.
- ETH_start 2y agoThis is not a phonetically friendly acronym.
- omernivro 2y agoThis is an interesting study. A similar permutation approach appears already in the Taylorformer paper (https://arxiv.org/pdf/2305.19141v1 https://arxiv.org/pdf/2305.19141v1). The authors use a Transformer decoder for continuous processes, like time series. During training, each sequence is shuffled randomly. Each sequence element has a positional encoding. Then, they use log-likelihood on the shuffled sequence. There, the permutation helps with predictions for interpolation, extrapolation and irregularly sampled data. Also, they show it helps with 'consistency', i.e., roughly the MSE is the same regardless of the generated order. What might this paper add to our understanding or application of these ideas? The idea of permuting the sequence order also appears in the Transformer Nerural Process paper: https://arxiv.org/pdf/2207.04179 https://arxiv.org/pdf/2207.04179.