Summary GateLoop Fully Data-Controlled Linear Recurrence for Sequence Modeling arxiv.org
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One Line
GateLoop is a sequence modeling model that outperforms others by maximizing linear recurrence potential, offering content-aware control and superior performance.
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Slide Presentation (10 slides)
Key Points
- GateLoop is a foundational sequence model that utilizes fully data-controlled linear recurrence to improve sequence modeling.
- GateLoop outperforms existing models for auto-regressive language modeling and offers a low-cost recurrent mode and an efficient parallel mode.
- GateLoop operates by incorporating data-controlled gating of inputs, hidden states, and outputs, allowing for content-aware control over forget- and retention behavior.
- GateLoop is compared to various models and outperforms them in terms of test perplexity on the WikiText103 benchmark for autoregressive language modeling.
- GateLoop offers practical benefits such as avoiding softmax-attention layers, eliminating the need for tedious initialization, and not requiring long implicit convolutions.
- The synthetic Memory Horizon dataset validates the advantage of GateLoop's data-controlled state transitions, significantly outperforming a model with fixed state transitions in terms of test accuracy.
- GateLoop's state transitions exhibit structured patterns, indicating deliberate utilization of data-controlled gating and forgetting/retention of memories.
- GateLoop demonstrates the effectiveness of fully data-controlled linear recurrence for sequence modeling, offering improved performance and practical advantages over existing models.
Summaries
18 word summary
GateLoop maximizes linear recurrence potential for sequence modeling, outperforming other models. It offers content-aware control and superior performance.
83 word summary
GateLoop is a sequence model that maximizes the potential of linear recurrence for sequence modeling. It outperforms other models for language modeling and offers low-cost recurrent and parallel modes. GateLoop incorporates data-controlled gating of inputs, hidden states, and outputs, providing content-aware control over forget- and retention behavior. It demonstrates superior performance on the WikiText103 benchmark and offers practical benefits such as avoiding softmax-attention layers and tedious initialization. The model showcases the ability to learn to forget memories input-dependently and exhibits structured state transitions.
145 word summary
GateLoop is a sequence model that utilizes fully data-controlled linear recurrence to enhance sequence modeling. It fills a gap in existing models by maximizing the potential of linear recurrence. GateLoop outperforms other models for auto-regressive language modeling and offers a low-cost recurrent mode and an efficient parallel mode. It incorporates data-controlled gating of inputs, hidden states, and outputs, allowing for content-aware control over forget- and retention behavior. The model can be trained efficiently using optimized associative scan implementations and provides data-controlled relative-positional information to attention. GateLoop demonstrates superior performance compared to various models on the WikiText103 benchmark. It also offers practical benefits such as avoiding softmax-attention layers, eliminating tedious initialization, and not requiring long implicit convolutions. The model showcases the ability to learn to forget memories input-dependently. In addition, GateLoop's state transitions exhibit structured patterns, showcasing deliberate utilization of data-controlled gating and forgetting/retention of memories.
352 word summary
GateLoop is a foundational sequence model that utilizes fully data-controlled linear recurrence to improve sequence modeling. Existing models have not fully taken advantage of the potential of linear recurrence, so GateLoop aims to fill this gap. The model generalizes linear recurrent models such as S4, S5, LRU, and RetNet by incorporating data-controlled state transitions. GateLoop outperforms existing models for auto-regressive language modeling and offers a low-cost recurrent mode and an efficient parallel mode. It also reveals implications for Transformer and other architectures.
GateLoop operates by incorporating data-controlled gating of inputs, hidden states, and outputs. It replaces the static state transition with time-varying state transitions, allowing for content-aware control over the forget- and retention behavior. The model can be trained efficiently using highly optimized associative scan implementations. GateLoop can also be interpreted as providing data-controlled relative-positional information to attention.
GateLoop is compared to other models such as S4, S4D, LRU, RetNet, Transformer, Hybrid H3, Performer, Reformer, Linear Attention, Transformer-XL, Hyena, and S5-Hyena. GateLoop outperforms these models in terms of test perplexity on the WikiText103 benchmark for autoregressive language modeling.
In addition to its performance advantages, GateLoop offers practical benefits such as avoiding softmax-attention layers, eliminating the need for tedious initialization, and not requiring long implicit convolutions. The model demonstrates the ability to learn to forget memories input-dependently, effectively vacating its hidden state for new relevant information.
The synthetic Memory Horizon dataset is designed to validate the advantage of data-controlled state transitions. The dataset requires models to memorize past input information back to the last reset token. GateLoop with fully data-controlled state transitions significantly outperforms a model with fixed state transitions in terms of test accuracy. The performance of the fully data-controlled variant is maintained for twice as long as the fixed variant as the required memory span increases.
GateLoop's state transitions exhibit structured patterns, indicating deliberate utilization of data-controlled gating and forgetting/retention of memories. Future work can explore different initialization strategies, amplitude- and phase-activations, and interpretability of the learned state transitions.
Overall, GateLoop demonstrates the effectiveness of fully data-controlled linear recurrence for sequence modeling, offering improved performance and practical advantages over existing models.