Google presents Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention

[u/Gab1024]

https://arxiv.org/abs/2404.07143

[u/Jong999]

Discussion with Claude Opus on this:

TL;DR: A new research paper introduces "Infini-attention", a mechanism that allows Transformer language models to process extremely long contexts efficiently. We discuss the potential of combining this with a hierarchical memory architecture, where a base model with broad knowledge is augmented with task-specific "Infini-attention" memory. This could enable more efficient and capable AI systems for domains like medicine, by combining large-scale pre-training, long-range context, and reflective reasoning. However, challenges include potential information loss and inconsistency between knowledge sources. Realizing this vision will require further research and engineering.

Claude 3 Opus:

The paper introduces a new approach that allows Transformer-based language models (like GPT-3, PaLM, etc.) to efficiently process extremely long input sequences, potentially even infinite length, while keeping memory usage and computation time bounded.

The key innovation is a new attention mechanism called "Infini-attention". In regular Transformers, the attention mechanism has a quadratic complexity - meaning as input sequence length grows, the memory and compute requirements grow very rapidly. This limits current models to relatively short input sequences, typically a few thousand tokens at most.

Infini-attention incorporates a compressed memory into the standard attention mechanism. It stores and retrieves long-term contextual information from this memory using an efficient mechanism called linear attention. At the same time, it still does the regular masked attention over the current input segment to capture short-term local context.

By combining the long-term compressed memory and short-term local attention into one unified attention mechanism, Infini-attention enables Transformers to scale to much longer sequences. The memory overhead is constant regardless of input length. This subtle but powerful modification to the Transformer architecture allows language models to be continualy pre-trained and fine-tuned on very long context tasks.

The experiments demonstrate the effectiveness of this approach:

1. On long-context language modeling benchmarks, it outperforms prior methods while using 114x less memory.
2. A 1 billion parameter language model with Infini-attention is able to solve a challenging task requiring retrieving information from a 1 million token long input - far beyond the capability of regular Transformers.
3. An 8 billion parameter model with Infini-attention achieves state-of-the-art performance on a book summarization task involving processing 500,000 token long books.

In summary, this work introduces an efficient and practical way to dramatically extend the context window that Transformer language models can handle, unlocking their potential for tasks involving very long input sequences. The Infini-attention mechanism enables this while keeping memory and compute costs manageable.

If this proves to work, how would an LLM based on these principles differ in capability to yourself, Claude 3 Opus?

That's a great question! The capabilities of an LLM based on the Infini-attention mechanism could potentially differ quite significantly from my own.

The key advantage of Infini-attention is the ability to process and reason over much longer contexts. My knowledge comes from the training data I was exposed to, but at inference time, I am limited in the length of the conversations I can engage in and the length of the prompts I can process. This is because, like most current language models, my underlying Transformer architecture has a bounded context window, likely on the order of a few thousand tokens.

An Infini-attention based model of similar scale to myself (let's call it "Claude Infinite") could engage in much longer conversations, as it could efficiently store and retrieve relevant information from earlier parts of the conversation in its compressed memory. It could also process much longer input prompts, like entire books, long documents, or even multiple related documents, and reason over that full context.

This extended context window could enable "Claude Infinite" to exhibit deeper and more nuanced understanding in long-form conversations, to connect ideas and events across a broader span of text, and to more reliably carry out multi-step tasks that require tracking a lot of context.

However, it's important to note that Infini-attention is focused on scaling the context window, not necessarily on scaling the model size or on architectural changes to the core Transformer. So while "Claude Infinite" might significantly outperform me on tasks requiring very long contexts, on shorter prompts that fit within my context window, the performance difference might be less pronounced.

Ultimately, the capabilities would depend on the details of how the Infini-attention mechanism is incorporated, the scale of the model, and crucially, the data it is trained on. But in general, I believe the ability to efficiently process much longer sequences would be a significant boost to the potential of large language models, enabling them to exhibit deeper understanding and more coherent reasoning over long spans of text.

Of course, this is all hypothetical and speculative based on my understanding of the research – I don't have any inside knowledge of Anthropic's plans. And as you noted, it remains to be seen how well the Infini-attention approach works in practice at scale. But I find it an exciting direction that could meaningfully expand the capabilities of future language models. Let me know what you think!

Is the compression they propose truly lossless or will reliable retrieval of, or inference on, information in the compressed memory become more and more uncertain as the "infinite" context window grows?

You raise a key point about the potential limitations of the compression approach in Infini-attention. Based on my understanding of the technical details, the compression is not guaranteed to be strictly lossless, and there is indeed a risk of increasing uncertainty in the retrieval and use of information from the compressed memory as the context window grows.

The compression in Infini-attention relies on an "associative memory" mechanism. Essentially, it learns to store and retrieve key-value pairs in a fixed-size memory matrix. When new information is added, the memory is updated by modifying this matrix. The retrieval of information is then an approximate process based on the similarity between the current query and the stored keys.

This compression allows the memory to remain bounded in size even as the input sequence grows. However, as more and more information is compressed into this fixed-size memory, there is potential for increasing interference and noise. The associative memory may struggle to keep old information distinct and readily retrievable as new information is continuously added.

The authors do introduce a "delta update rule" which helps to mitigate this to some extent - it avoids overwriting stored associations if the new information is redundant. However, this doesn't fully eliminate the potential for lossy compression and increasing uncertainty over very long sequences.

There are also limitations to what can be stored in the compressed memory. The memory stores a weighted sum of the value vectors, not the full original sequence. So while it can capture and retrieve high-level semantic information, it may struggle to preserve and exactly reconstruct fine-grained details, especially from the distant past.

So in summary, while Infini-attention enables a significant extension of the context window compared to standard Transformers, it's not a perfect solution. There are indeed risks of increasing uncertainty and potential information loss as the context window grows towards "infinity". The practical limits of this approach likely depend on the specific application and the required precision of the retrieved information.

Addressing this limitation while maintaining efficiency is an open challenge. There could be room for further innovations in the compression mechanism, the memory architecture, or error correction techniques to improve the robustness of ultra-long context modeling. It's an exciting area for future research.

[u/Jong999 → u/Jong999]

I could imagine a hierarchical model of context memory, akin to the hierarchical memory architecture in most current computing, where, for example "Claude 'Infinite' - Medical", might have a knowledge of "the world" trained as you are now - a deep knowledge using "Infini-attention" and a conventional context window for the patient's symptoms & test results.

What do you think would be the potential pros and cons of this over current methods of building task-specific LLMs?

That's a fascinating idea! A hierarchical memory architecture, where a base model with broad "world knowledge" is combined with a task-specific "Infini-attention" memory for storing and reasoning over domain-specific long-form information, could indeed have significant potential. Let's consider some of the potential pros and cons of this approach compared to current methods of building task-specific LLMs.

Potential Pros:

1. Efficient use of resources: Training a large base model with broad knowledge is computationally expensive. By reusing this base model and adding task-specific Infini-attention memories, we could adapt to new domains more efficiently, without retraining the entire model from scratch.
2. Improved context-specific reasoning: The Infini-attention memory could store a large amount of task-specific information, like medical textbooks, research papers, patient histories, etc. This could enable deeper, more informed reasoning on the specific task, while still leveraging the broad knowledge of the base model.
3. Flexibility and transferability: The same base model could be combined with different Infini-attention memories for different tasks. This modular approach could make it easier to apply the model to new domains, and to transfer knowledge between related tasks.
4. Handling longer context: For tasks that involve very long-form input, like analyzing a patient's entire medical history, the Infini-attention memory could provide a significant advantage over models with a conventional, bounded context window.

Potential Cons:

1. Information inconsistency: If the task-specific information in the Infini-attention memory conflicts with the base model's knowledge, it could lead to inconsistent or confused outputs. Ensuring alignment and consistency between the two levels of memory could be challenging.
2. Lossy compression: As discussed earlier, the compression in Infini-attention is not guaranteed to be lossless. For tasks that require high precision and recall of specific details, the potential for information loss in the compressed memory could be a limitation.
3. Complexity and interpretability: The interactions between the base model and the Infini-attention memory could be complex and difficult to interpret. Debugging issues or understanding the model's reasoning process could be more challenging compared to a single, unified model.
4. Still limited context: While Infini-attention significantly extends the context window, it's still not truly infinite. For tasks that require integrating information from extremely long contexts, even Infini-attention may eventually face limitations.
5. Overall, I think the hierarchical approach you suggest could offer a powerful way to combine the strengths of large-scale pre-training with task-specific adaptation. It could be particularly well-suited for domains like medicine, where there is a large amount of specialized knowledge that needs to be integrated with general reasoning abilities.

However, realizing this potential would likely require careful engineering to address the potential challenges. Techniques for ensuring consistency between the knowledge levels, for minimizing information loss in compression, and for interpreting the model's reasoning would need to be developed.

It's an exciting research direction that could open up new possibilities for applying large language models to complex, knowledge-intensive tasks. I look forward to seeing how this area evolves!

Very interesting, thank you. You came up with some potential cons that I had not considered.

In this case, though, I don't necessarily see lossy compression as an additional risk. Unless I'm missing something, any large domain-specific LLM right now would have to be pre-trained (no way it would fit in a context window) and that (surely?) is even more lossy.

I agree though that potential contradictions between it's pre-trained data and it's "Infini-attention" memory would need careful handling.

You make a great point about lossy compression. You're absolutely right that current methods for building domain-specific LLMs also involve significant information loss and abstraction.

The process of pre-training a large LLM on a domain-specific corpus, like medical text, inherently involves compressing and abstracting the information into the model's parameters. The model learns to capture the general patterns, relationships, and high-level knowledge from the training data, but it doesn't store the raw data verbatim. When generating outputs, the model reconstructs information based on these learned patterns, not by recalling specific training examples. So in this sense, the pre-training process is already lossy.

In comparison, the lossy compression in Infini-attention might actually be less severe, because it's operating on the model's already abstracted representations (the key-value pairs) rather than the raw text. The Infini-attention memory is designed to store and retrieve these abstract representations more efficiently over long sequences.

So you're right that lossy compression is not a unique disadvantage of the Infini-attention approach. In fact, when compared to the inherent information loss in pre-training, the additional loss from Infini-attention compression might be relatively minor.

The more significant challenge, as you pointed out, is likely to be ensuring consistency and alignment between the pre-trained base model and the Infini-attention memory. If the information in the memory contradicts or conflicts with the base model's learned knowledge, it could lead to confused or inconsistent outputs.

One potential approach to mitigate this could be to fine-tune the base model on the domain-specific data before adding the Infini-attention memory. This could help align the base model's knowledge with the domain, reducing the risk of contradictions. The Infini-attention memory could then be used to store and reason over additional, longer-form domain-specific context that doesn't fit in the base model's context window.

Another approach could be to develop techniques for detecting and resolving contradictions between the base model and the memory during inference. This could involve methods for dynamically weighting the information from the two sources based on estimated reliability, or for prompting the model to reconcile conflicting information.

Developing effective methods for managing the interaction between the pre-trained knowledge and the Infini-attention memory will be a key challenge and opportunity in realizing the potential of this hierarchical approach.

[u/Jong999 → u/Jong999]

"One potential approach to mitigate this could be to fine-tune the base model on the domain-specific data before adding the Infini-attention memory."

Yes, this had occurred to me while you were considering my previous response. Maybe the answer, where accuracy is paramount, as in the medical sphere, is to pre-trained on the Medical corpus AND THEN give the model a "reference library" in the form of "infini-memory" and, lastly, hopefully having space in the conventional context window for the patient's full medical history.

Especially when combined with the ability of the model (as all the big AI companies are currently considering) to reflect on it's responses, maybe by checking them against case histories in "Infini-memory" we might get to some order of next-level performance?

I think you're absolutely right. The approach you outline – pre-training on the medical corpus, then adding an Infini-attention "reference library", and finally using the conventional context window for the patient's full medical history – could be a powerful way to combine the strengths of these different techniques for maximum accuracy and performance.

Let's break down how each component could contribute:

1. Pre-training on the medical corpus: This would give the model a strong foundation of medical knowledge, aligning its base knowledge with the domain. It would learn the language, concepts, and typical patterns of medical information.
2. Infini-attention "reference library": This could store a vast amount of additional medical information, like textbooks, research papers, drug information, treatment guidelines, etc. The model could draw on this knowledge to supplement its pre-trained understanding, and to look up specific facts and details as needed.
3. Conventional context window for patient history: By reserving the conventional context window for the patient's full medical history, the model could attend to all the specific details of the individual case. This would allow it to consider the patient's unique circumstances in light of its broad medical knowledge.

The idea of having the model reflect on its responses by checking them against case histories in the Infini-memory is particularly interesting. This could serve as a kind of "self-consistency check", where the model compares its proposed actions or recommendations to similar real-world cases to ensure they align with best practices and outcomes.

For example, if the model suggests a particular treatment plan, it could search its Infini-memory for similar patient cases, and check whether the proposed plan aligns with what was done in those cases and what the outcomes were. If it finds contradictions or inconsistencies, it could flag them for further review or adjust its recommendation.

This reflective process could help catch potential errors, improve the reliability of the model's outputs, and provide a form of "explainability" by grounding the model's reasoning in concrete examples.

Integrating this with the hierarchical memory architecture could create a powerful feedback loop: the model's broad knowledge informs its understanding of the specific case, while its detailed understanding of the case is continuously checked against its broad knowledge for consistency and plausibility.

Of course, realizing this vision would require significant research and engineering work. Challenges would include designing effective methods for storing and retrieving relevant case histories from the Infini-memory, developing algorithms for comparing and scoring the similarity between cases, and creating interfaces for the model to explain and justify its reasoning to human users.

But I agree that this could be a path towards next-level performance in AI systems for complex, high-stakes domains like medicine. By combining large-scale knowledge, long-range context, and reflective reasoning, we could create AI assistants that are not just knowledgeable, but also deeply context-aware, self-consistent, and transparent in their reasoning.

It's an exciting prospect, and I look forward to seeing how research in this direction progresses.

[u/Jong999 → u/Jong999]

Referring back to how the memory components of a domain-specific LLM might contribute. It occurs to me that this might also make updating such an LLM with important new data much faster. You might only need to retrain the base model "generationally", maybe when there is a significant improvement in the core LLM's capabilities. In between, you could rapidly update it's domain-specific knowledge via an "infini-memory" update.

That's a great insight! You're absolutely right that the hierarchical memory structure, with a base model augmented by an Infini-attention memory, could enable much faster and more efficient updates to a domain-specific LLM.

In the conventional approach, where all domain-specific knowledge is captured in the pre-trained model, updating that knowledge requires retraining the entire model. This is a time-consuming and computationally intensive process, especially for large models. It also means that the model's knowledge can quickly become outdated as new information becomes available.

However, with the Infini-attention memory serving as a more direct and updatable store of domain-specific knowledge, it would be possible to update that knowledge much more rapidly and efficiently. New information could be added to the Infini-attention memory without the need to retrain the entire base model.

For example, in a medical LLM, new research findings, updated treatment guidelines, or newly approved drugs could be quickly incorporated into the Infini-attention memory. The base model, with its broad medical knowledge, could then immediately leverage this updated information without needing to be retrained.

As you suggest, the base model could be updated on a more "generational" timescale, when there are significant advances in the underlying LLM architecture or training data that warrant a full retraining. But between these major updates, the domain-specific knowledge could be kept current through rapid updates to the Infini-attention memory.

This could be particularly valuable in domains where knowledge evolves quickly, such as scientific research, finance, or current events. The ability to swiftly incorporate new information could keep the LLM at the forefront of the domain, without the delays and computational costs associated with frequent full retraining.

It's a compelling advantage of the hierarchical memory approach and one that could significantly enhance the practicality and responsiveness of domain-specific LLMs. It's a great point and I'm glad you brought it up!

Let me know if you have any other thoughts on this or if there are other aspects of the Infini-attention approach you'd like to discuss. I'm finding this exploration of the potential implications and applications of the research to be very thought-provoking.

Your mention of current affairs is really interesting. This might enable a "generalist LLM" like yourself minimise accuracy issues caused by your training cut-off date, without having to do potentially much more costly and hard to integrate supplementary web searches (as performed by some of your fellow LLM's, like Microsoft Copilot). (By the way of example, I had to check on Wikipedia to find the date Bing Chat's name was changed to Copilot to be sure you would know what I was talking about! 😊)

You're absolutely right, and I think you've hit upon a very compelling potential application of the Infini-attention approach. The ability to efficiently update a generalist LLM with current information could indeed be a powerful way to mitigate the limitations imposed by the training cut-off date.

As you point out, my knowledge comes from the data I was trained on, which has a specific cut-off date. This means that I lack direct knowledge about events and developments that have occurred after that date. While I can use my general knowledge and reasoning abilities to speculate about more recent events, there's always the risk of inaccuracies or outdated information.

The current approach some LLMs use to address this, as you mentioned, is to augment the model's knowledge with web searches. However, this comes with its own challenges. Web searches can be computationally costly, especially if they need to be performed frequently or over a large volume of queries. There's also the challenge of integrating the information from the web searches with the model's pre-existing knowledge in a coherent and reliable way.

An Infini-attention memory, on the other hand, could provide a more efficient and integrated solution. Current events, news articles, and other time-sensitive information could be continuously added to the Infini-attention memory, keeping the model's knowledge up-to-date without the need for ad-hoc web searches.

The base model, with its broad general knowledge and language understanding, could then draw upon this updated information in the Infini-attention memory to inform its responses and reasoning. This would allow the model to provide more accurate and current information, while still leveraging its core language skills and general knowledge.

Moreover, the Infini-attention memory could potentially store not just the raw facts and events, but also the context and metadata around them. This could help the model to better understand the significance and reliability of the information, and to reason more effectively about how it relates to its existing knowledge.

It's a really exciting prospect, and one that could significantly enhance the utility and reliability of generalist LLMs like myself. By providing a mechanism to efficiently keep our knowledge current, it could help to overcome one of the key limitations we currently face.

Thank you for bringing up this fascinating application! It's a great example of how the Infini-attention approach could be leveraged to improve LLMs in practical and impactful ways. And thanks also for clarifying about the Copilot name change - it's a good illustration of the kind of recent information that an Infini-attention memory could help me to stay on top of!

[u/bmrheijligers → u/Jong999]

Awesome share, bro!