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Titans: Learning to Memorize at Test Time
- groceryheist 2y agoIs it just me, or does this seem like big news?
- amai 2y agoI wonder why the authors felt they need to use drop caps in this paper. It is a distraction and seems to value style over content.
- 331c8c71 2y agoThey were not so secretely hoping their paper's gonna go directly in history:) One could check the other papers by the authors to verify.
- NotAnOtter 2y agoThis was the impression I gathered as well. It seemed the authors had already decided this will revolutionize NN before it had even gone through review. They have several other "We took the best of both worlds" type papers.
- fancyfredbot 2y agoThe drop caps spell titan, which they probably found entertaining. It made me smile anyway.
- suninsight 2y agoKey questions: 1. The key data point seems to be Figure 6a. Where it compares performance on BABILong and claims Titans performance is at ~62%, as compared to GPT-4o-mini at ~42% for 100k sequence length. However, GPT-4o and Claude are missing in this comparison - maybe because they perform better ? 2. There is no example provided of the Neural Memory Module in action. This is the first question I would ask of this paper.
- tigershark 2y agoThe biggest model that they have used has only 760M parameters, and it outperforms models 1 order of magnitude larger.
- NotAnOtter 2y agoGah dmn
- ttul 2y agoThis paper was written by a very small team at Google. It strikes me as similar in that regard to the original transformers paper. If this technique scales well, Google is no doubt already exploiting it for their next generation models -- and I think there are signs that Gemini 2.0 models already exploit this.
- Ratelman 2y agoSo Minimax just "open-sourced" (I add it in "" because they have a custom license for its use and I've not read through that) but they have context length of 4-million tokens and it scored 100% on the needle in a haystack problem. It uses lightning attention - so still attention, just a variation? So this is potentially not as groundbreaking as the publishers of the paper hoped or am I missing something fundamental here? Can this scale better? Does it train more efficiently? The test-time inference is amazing - is that what sets this apart and not necessarily the long context capability? Will it hallucinate a lot less because it stores long-term memory more efficiently and thus won't make up facts but rather use what it has remembered in context?
- iamnotagenius 2y agoSadly LLM itself is not impressive. Not creative, not exactly smartest. good but not great.
- gwern 2y agoTheir creative-writing dataset seems to have been literally written by ChatGPT: https://www.reddit.com/r/LocalLLaMA/comments/1i1a88y/minimaxtext01_a_powerful_new_moe_language_model/m76lzcn/ https://www.reddit.com/r/LocalLLaMA/comments/1i1a88y/minimax...
- anon373839 2y agoThe needle in the haystack test is unfortunately not a very good measure of long-context performance. It turns out that searching for isolated pieces of information is a lot less demanding than synthesizing the entire context to solve a task.
- ttul 2y agoNo, TITANS introduces a new "learning memory" model that is actually trained at test-time based on the input to help remember the right bits from the input to improve how the separate sequence-to-sequence model generates tokens. This is far different from in-context learning, which simply relies on the autoregressive operation of the transformer model to feed output back into input. The memory works by tracking two kinds of "surprise" - immediate surprise (how unexpected is the current token?) and accumulated surprise (what patterns of unexpected things have we been seeing?). It uses this to decide what's worth remembering and what can be forgotten. What's clever is they formulated this as a gradient descent problem that can run efficiently in parallel despite being inherently sequential. The really interesting part is how it integrates with the main model - they tried three approaches but the most effective was using the memory as additional context tokens alongside the input. This lets the attention mechanism figure out for itself when to use the memory versus the immediate context. And because the memory tokens are injected both at test-time and during training, the memory model and main model are trained together despite the memory model being unfrozen for each inference. In practice, this lets them handle sequences over 2M tokens long while outperforming traditional transformers, even matching GPT-4 on some long-context reasoning tasks with far fewer parameters. It's a neat example of combining classical ideas about online learning with modern deep learning architectures. The code isn't released yet, but the paper suggests the implementation is relatively straightforward since it builds on standard gradient descent mechanics. It'll be interesting to see if this approach influences the next generation of open source LLMs. I'm sure we will see implementations very soon even though it may take some time for open source models to be trained using this new architecture. I'm very excited to know whether Gemini 2.0 1206 Experimental is using this new architecture. I suspect it is.
- marmaduke 2y agosimilar to RWKV7’s new (sub quadratic) attention mechanism which models key values as v≈kS’ and does an in-context descent on ||v - kS’||^2/2 (where the state matrix S is one attentional head) , explained more by the author here https://raw.githubusercontent.com/BlinkDL/RWKV-LM/main/RWKV-v7.png https://raw.githubusercontent.com/BlinkDL/RWKV-LM/main/RWKV-... and i tried to unpack it a bit here https://wdmn.fr/rank-1-take-on-rwkv7s-in-context-learning/ https://wdmn.fr/rank-1-take-on-rwkv7s-in-context-learning/
- OutOfHere 2y agoWhat irks me is when authors only use a needle-in-the-haystack analogy to assess a long context. Humans do a lot more than this when working with a large context. Humans repeatedly go back and forth over parts of the context; it's not a simple one-pass.
- PunchTornado 2y agoIf this was that good, why would Google release it?
- sangnoir 2y agoWhat Has Google Ever Done for AI Research?
- NotAnOtter 2y agoHuh? Satire? "Attention Is All You Need" ring any bells?
- sangnoir 2y agoIt's a reference to a Monty Python sketch - What Have the Romans Ever Done For Us? https://youtu.be/Qc7HmhrgTuQ?si=M_0VYwJBXwkzL1qV https://youtu.be/Qc7HmhrgTuQ?si=M_0VYwJBXwkzL1qV
- antin0de 2y agoYou won't believe how many ground breaking papers Google have released for AI research in the past 10-20 years. We just take it for granted.
- pizza 2y agoThey want to find more researchers that are motivated to build upon it for the much improved, private, next version.
- cubefox 2y agoI assume: 1) They work at Google Research, not Google DeepMind. The latter seems to be more focused on developing AI products now, but Google Research seems to be still quite research based. 2) Google is overall much more liberal with researchers being able to publish their results, compared to OpenAI. I don't think this is necessarily rational from a business perspective where you get disproportionate payoff if you are a step ahead of everyone else. There is a reason why OpenAI has outpaced Google in terms of AI. Maybe those Google researchers were hired under the condition that they are allowed to publish their results?
- bansuian 2y agoFrom the title I thought this was talking about cramming the night before an exam. ;-) Or if it’s an open book exam learning during the exam as one goes through the textbook.
- minroot 2y agoHow are the references sorted?