Addressing Some Limitations of Transformers with Feedback Memory

Abstract: Transformers have been successfully applied to sequential, auto-regressive tasks despite being feedforward networks. Unlike recurrent neural networks, Transformers use attention to capture temporal relations while processing input tokens in parallel. While this parallelization makes them computationally efficient, it restricts the model from fully exploiting the sequential nature of the input. The representation at a given layer can only access representations from lower layers, rather than the higher level representations already available. In this work, we propose the Feedback Transformer architecture that exposes all previous representations to all future representations, meaning the lowest representation of the current timestep is formed from the highest-level abstract representation of the past. We demonstrate on a variety of benchmarks in language modeling, machine translation, and reinforcement learning that the increased representation capacity can create small, shallow models with much stronger performance than comparable Transformers.

• The paper introduces the Feedback Transformer architecture, which enhances the standard Transformer model by incorporating a feedback memory mechanism to access historical representations.
• Empirical evaluations show the Feedback Transformer outperforms standard models on tasks requiring long-term dependency tracking, including language modeling, translation, and reinforcement learning benchmarks like Wikitext-103 and WMT14 En-De.
• This recursive architecture offers an elegant solution to Transformer limitations, emulating RNN benefits for state tracking and potentially enabling more potent models for applications like code execution or dialogue systems.

Analysis of "Addressing Some Limitations of Transformers with Feedback Memory"

The paper entitled "Addressing Some Limitations of Transformers with Feedback Memory" proposes an innovative architecture enhancement to the established Transformer model, a cornerstone of sequential and autoregressive tasks in NLP. The authors introduce the Feedback Transformer, which integrates a feedback memory mechanism to overcome key limitations inherent in conventional Transformer architectures.

Key Contributions and Methodology

The central contribution of the paper is the Feedback Transformer architecture, which alters the traditional processing structure of a Transformer. By leveraging a feedback mechanism, the model facilitates access to historical high-level representations when computing current timestep representations. This approach allows for recursive computation, enhancing the model's capacity to handle long sequences and complex structures more efficiently than standard Transformer models.

The methodology pivots around adjusting the self-attention mechanism to focus on a shared memory of past computations. The memory aggregates and merges hidden states from all layers into a single vector at each timestep, which subsequent layers can then access. This modification enables recursive updates and captures sequential dependencies more like Recurrent Neural Networks (RNNs) but with the added advantage of a substantial memory buffer that is not constrained by layer depth.

Results and Empirical Evaluation

Empirical results showcase the efficacy of the Feedback Transformer across several benchmarks in language modeling, translation, and reinforcement learning tasks. The researchers observe that their model can outperform standard Transformers, particularly in scenarios where tracking of long-term dependencies or recursive computation is paramount.

• Language Modeling and Translation: The Feedback Transformer model displays enhanced performance capabilities on Wikitext-103 and WMT14 En-De datasets. Particularly noteworthy is the model's ability to maintain strong performance even with reduced model depth, signifying efficient abstraction and representation capacity with fewer layers.
• Reinforcement Learning: Within reinforcement learning environments—exemplified by the corridor and maze navigation tasks—the Feedback Transformer distinctly outperforms its counterparts by accurately maintaining and updating belief states over extended timeframes, highlighting its robust memory handling.

The Feedback Transformer achieves state-of-the-art results in specific scenarios with relatively smaller models, crucial as it offers reduced computational resource demands during both training and inference.

Implications and Future Directions

The paper's results indicate that recursive architectures, like the Feedback Transformer, can significantly benefit certain classes of sequential tasks where memory and state tracking are critical. By transcending inherent limitations tied to the fixed transformations of the Transformer model, Feedback Transformers can emulate the advantages of RNNs while retaining computational efficiencies of Transformers due to parallel processing.

The implications are substantial; applications demanding intricate state updates—such as code execution or long-form text generation—could greatly benefit from this architecture. Future research could explore hybridizing this model with existing structural adaptations of Transformers to fully exploit their capabilities in dynamic contexts, such as dialogue systems or real-time translation.

Overall, the Feedback Transformer represents an elegant solution to pre-existing architectural bottlenecks, opening pathways for developing even more potent neural networks capable of complex sequential understanding without sacrifices in computational efficiency.