Masked Mixers for Language Generation and Retrieval

🌈 Abstract

The paper explores the use of masked mixers, which replace self-attention with masked convolutions, as an alternative to transformers for language modeling tasks. The key findings are:

• Masked mixers exhibit much more accurate input representation compared to transformers, especially for non-self tokens.
• Masked mixers are generally similarly efficient as transformers for causal language model training, but a transformer-masked mixer hybrid is the most efficient.
• Embeddings from masked mixers are far superior to those from transformers for language retrieval tasks.

🙋 Q&A

[01] Accurate self and non-self token representation in Masked Mixers

1. How do the authors measure the information present in a model's hidden layer representation of the input? The authors use a gradient descent-based approach to recover the input from the hidden layer activations. They optimize the embedding of the input rather than the input itself, and then convert the optimized embedding back to the input using the Moore-Penrose pseudo-inverse.

2. How do masked mixers compare to transformers in terms of input representation accuracy? Masked mixers exhibit near-perfect representation before training, and larger models retain this characteristic even after training. In contrast, transformers exhibit very poor input representation, with the information present in the last hidden layer being incapable of accurately recovering the input.

3. How do masked mixers perform in terms of non-self token representation compared to transformers? Masked mixers are able to accurately represent a limited number of non-self tokens, while transformers fail to accurately represent non-self tokens even for small context windows.

[02] Masked Mixer and Transformer learning efficiencies

1. How do the training efficiencies of masked mixers and transformers compare? Flat masked mixers are more efficient learners than expanded masked mixers and also more efficient than modern transformers with default hyperparameters. However, optimized versions of modern transformers are somewhat more efficient learners than optimized masked mixers.

2. What is the most efficient learning algorithm observed for the TinyStories dataset? The most efficient learning algorithm observed is a transformer-masked mixer hybrid, suggesting that these models learn in an orthogonal manner.

3. How do early transformer implementations compare to masked mixers in terms of learning efficiency? Masked mixers are more efficient learners than early transformer implementations of all-next-token causal language modeling.

[03] Masked mixers are more effective for retrieval than transformers

1. What is the key hypothesis regarding the suitability of attention for retrieval tasks? The authors hypothesize that attention is not well-suited for retrieval tasks because the attention transformations are biased towards non-invertibility, resulting in a many-to-one mapping that is detrimental to retrieval.

2. How do embeddings from masked mixers compare to embeddings from transformers for retrieval tasks? Embeddings from masked mixers are found to result in far better summary-to-story retrieval compared to embeddings from transformers.

3. Can untrained or non-CLM trained models' embeddings be used effectively for retrieval? No, the authors find that embeddings from untrained masked mixers or mixers trained on non-CLM tasks perform poorly for retrieval, suggesting that the CLM training endows the embedding model with the ability to capture important aspects of the language input.

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