Summary ModuleFormer Modularity Emerges from Mixture-of-Experts arxiv.org
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ModuleFormer is a modular neural network architecture that improves large language models by enabling module insertion and expert pruning, resulting in comparable performance to dense language models but with reduced latency.
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Key Points
- ModuleFormer is a new neural network architecture that uses modularity to improve efficiency and flexibility of large language models.
- ModuleFormer is based on the Sparse Mixture of Experts (SMoE) and allows for the insertion of new modules and expert pruning.
- ModuleFormer achieves the same performance as dense language models with lower latency and a smaller memory footprint.
- Stick-breaking attention is used in ModuleFormer to encode position information and simplify length-extrapolation of self-attention.
- ModuleFormer includes load balancing techniques during pretraining to avoid wasting module capacity and maximize mutual information between tokens.
Summaries
34 word summary
ModuleFormer is a neural network architecture that enhances large language models by introducing modularity, allowing for module insertion and expert pruning. It achieves the same performance as dense language models but with lower latency.
45 word summary
The paper introduces a new neural network architecture called ModuleFormer that improves the efficiency and flexibility of large language models through modularity. ModuleFormer allows for the insertion of new modules and expert pruning, achieving the same performance as dense language models but with lower latency
430 word summary
The paper proposes a new neural network architecture called ModuleFormer that leverages modularity to improve the efficiency and flexibility of large language models. ModuleFormer is based on the Sparse Mixture of Experts (SMoE) and includes two types of modules:
ModuleFormer is a model that allows for the insertion of new modules and expert pruning. It achieves the same performance as dense language models (LLMs) with lower latency and a smaller memory footprint, allowing it to process more tokens per second. ModuleFormer
Kirkpatrick et al. proposed a regularization method to address a specific phenomenon. Munkhdalai and Yu, as well as Beaulieu et al., have also developed methods related to lifelong learning and can be combined with other approaches. Neural
The excerpt describes the attention output computation and the use of stick-breaking attention to encode position information and simplify length-extrapolation of self-attention. It also discusses load balancing during pretraining to avoid wasting module capacity, maximizing mutual information between tokens and
Table 2 provides information on the inference speed, memory consumption, and throughput of different models. The measurements were taken on an A100 80GB GPU with a batch size of 32 and a sequence length of 1024 tokens. The models
The document discusses the concept of modularity in the context of mixture-of-experts models. The authors collected expert activation frequencies for MLP experts on different domains of the Pile test set and computed the KL-divergence between domains for two models: Mo
Sparse models experience less interference and have better performance in terms of full finetuning compared to non-sparse models. The proposed ModuleFormer architecture demonstrates consistently better results in efficient tuning compared to the baseline. Continual lifelong pre-training experiments are conducted on models
We propose ModuleFormer, a modular architecture that includes stick-breaking attention heads, mutual information load balancing loss for pretraining, and load concentration loss for finetuning. We pretrained a language model called MoLM using ModuleFormer and found that it achieves the
The summary includes a list of citations for various papers and articles related to language models, code evaluation, continual learning, modular multi-task learners, and language modeling. The citations cover topics such as large language models, catastrophic forgetting, mixture of experts, scaling
This summary provides a concise version of the text excerpt while preserving important details and highlighting key points.
The excerpt includes a list of references to various research papers and preprints. Some of the topics covered in these papers include overcoming catastrophic forgetting in neural networks,
Low-rank bases for factorized hidden layer adaptation of dnn acoustic models. In 2016 IEEE Spoken Language Technology Workshop (SLT), pages 652-658. IEEE, 2016. Noam Shazeer, Azalia