Feedback Attention Memory (FAM): A Novel Transformer Architecture for Efficient Long-Context Processing

Keskeiset käsitteet

Feedback Attention Memory (FAM) is a novel Transformer architecture that leverages a feedback loop to enable the network to attend to its own latent representations, fostering the emergence of working memory within the Transformer and allowing it to process indefinitely long sequences efficiently.

Tiivistelmä

The paper proposes a novel Transformer architecture called Feedback Attention Memory (FAM) that addresses the limitations of standard Transformers in handling long input sequences.
Key highlights:

Transformers suffer from quadratic complexity in attention, which limits their ability to process infinitely long inputs.
FAM introduces a feedback loop that allows the Transformer to attend to its own latent representations, enabling the emergence of working memory.
FAM integrates seamlessly with pre-trained Transformer models without adding new weights.
Experiments show that FAM significantly improves Transformer performance on long-context tasks across various model sizes (1B, 8B, and 24B).
FAM can maintain past information for an indefinite horizon, making it a promising solution for large language models (LLMs) to handle infinitely long input sequences.
FAM outperforms Block Sliding Window Attention (BSWA) on long-context tasks, demonstrating its ability to effectively compress and retain important contextual information within extremely long contexts.
The paper also explores the connection between attention and working memory, drawing insights from neuroscience.

Tilastot

Transformers have quadratic complexity with respect to context length, which limits their ability to model long contexts.
Sliding Window Attention (SWA) and Block Sliding Window Attention (BSWA) are introduced to handle infinitely long sequences as input, but they have a limited receptive field.
The theoretical receptive field of BSWA is approximately equal to model depth × window size.

Lainaukset

"Feedback connections are prevalent in biological neural networks. Even organisms with simple neural structures, such as C. elegans (with only 302 neurons) (White et al., 1986), exhibit various feedback loops, like connections from higher-level interneurons to lower-level ones (Hasani et al., 2018)."
"In the human brain, working memory (Wallace, 1960) provides a temporary memory for performing tasks. While working memory is stored in sustained activations, long-term memory is stored in weights (Fuster, 1973)."

Tärkeimmät oivallukset

TransformerFAM: Feedback attention is working memory

by Dongseong Hw... klo arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09173.pdf

Syvällisempiä Kysymyksiä

How can the FAM architecture be further improved to better capture and retain long-term dependencies in the input sequences?

The Feedback Attention Memory (FAM) architecture can be enhanced in several ways to improve its ability to capture and retain long-term dependencies in input sequences. One approach could involve incorporating a more sophisticated mechanism for information compression within the feedback loop. By optimizing the compression process, FAM can effectively distill and retain essential information from past blocks while discarding redundant or less relevant details. Additionally, introducing adaptive mechanisms to adjust the attention weights based on the importance and relevance of past information could further enhance the model's ability to retain long-term dependencies. Furthermore, exploring different strategies for initializing and updating the feedback memory could lead to more efficient storage and retrieval of contextual information across blocks. By fine-tuning these aspects of the architecture, FAM can better capture and retain the intricate dependencies present in long input sequences.

What are the potential limitations or drawbacks of the FAM approach, and how can they be addressed?

While the Feedback Attention Memory (FAM) architecture offers significant advantages in processing long-context sequences, there are potential limitations and drawbacks that need to be addressed. One limitation could be the increased computational complexity and memory requirements associated with maintaining and updating the feedback memory across blocks. This could lead to scalability issues, especially when dealing with extremely long input sequences. To mitigate this, optimizing the memory management and updating mechanisms within FAM can help reduce the computational overhead and memory footprint. Additionally, the risk of information loss or distortion during the compression and propagation process in the feedback loop is another potential drawback. Implementing more robust error correction mechanisms and regularization techniques can help mitigate these risks and ensure the fidelity of the retained information. Furthermore, the interpretability of the feedback mechanism and its impact on model performance could be a challenge. Developing comprehensive visualization tools and diagnostic methods to analyze the functioning of the feedback loop can address this limitation and provide insights into the model's decision-making process.

How can the insights from the connection between attention and working memory in the brain be leveraged to develop more biologically-inspired neural network architectures for efficient long-context processing?

The insights from the connection between attention and working memory in the brain can inspire the development of more biologically-inspired neural network architectures for efficient long-context processing. By mimicking the brain's mechanisms for attention and memory retention, neural network architectures can be designed to exhibit similar capabilities in processing long-context sequences. One approach is to incorporate recurrent feedback loops within the network, allowing for the continuous updating and propagation of contextual information across blocks. This emulates the sustained activations observed in working memory systems in the brain. Additionally, integrating multi-sensory integration principles into the architecture can enable the fusion of diverse data modalities, enhancing the model's ability to process heterogeneous inputs efficiently. Furthermore, leveraging the brain's distributed processing and parallelism can inform the design of neural network architectures that optimize resource utilization and computational efficiency in handling long-context tasks. By drawing inspiration from the brain's attention and memory mechanisms, researchers can develop neural network models that not only excel in long-context processing but also exhibit cognitive-like capabilities in information retention and utilization.