Multiplicative LSTM for sequence modelling (1609.07959v3)

Abstract: We introduce multiplicative LSTM (mLSTM), a recurrent neural network architecture for sequence modelling that combines the long short-term memory (LSTM) and multiplicative recurrent neural network architectures. mLSTM is characterised by its ability to have different recurrent transition functions for each possible input, which we argue makes it more expressive for autoregressive density estimation. We demonstrate empirically that mLSTM outperforms standard LSTM and its deep variants for a range of character level LLMling tasks. In this version of the paper, we regularise mLSTM to achieve 1.27 bits/char on text8 and 1.24 bits/char on Hutter Prize. We also apply a purely byte-level mLSTM on the WikiText-2 dataset to achieve a character level entropy of 1.26 bits/char, corresponding to a word level perplexity of 88.8, which is comparable to word level LSTMs regularised in similar ways on the same task.

• The paper presents the Multiplicative Long Short-Term Memory (mLSTM) architecture, which merges LSTM and multiplicative RNNs to achieve enhanced sequence modeling through input-dependent transitions.
• mLSTM demonstrates superior empirical performance over standard LSTM variants in character-level language modeling tasks, achieving competitive bits per character results on standard datasets.
• The mLSTM architecture simplifies model construction and enhances parallelizability, showing strong performance despite lacking non-linear recurrent depth commonly used in other models.

Multiplicative LSTM for Sequence Modelling: An Expert Analysis

The paper presents a novel recurrent neural network (RNN) architecture called Multiplicative Long Short-Term Memory (mLSTM), merging the LSTM architecture with multiplicative RNNs to enhance sequence modeling efficiencies. The primary objective is to create a model that facilitates input-dependent transitions, thereby expanding expressiveness in autoregressive density estimation.

Key Contributions

The research introduces mLSTM, arguing for its increased expressiveness attributed to input-dependent recurrent transition functions. It validates this claim through empirical comparisons in character-level LLMing tasks, demonstrating mLSTM's superior performance over standard LSTM variants, as illustrated in several datasets.

• Expressiveness: Unlike conventional RNNs or LSTMs, mLSTM dynamically adjusts its transition functions per input, offering a versatile system capable of adapting to unexpected sequences without significant performance degradation.
• Empirical Performance: The mLSTM achieved 1.27 bits per character on text8 and 1.24 bits per character on Hutter Prize, also obtaining a word-level perplexity of 88.8 on WikiText-2. These results underscore mLSTM's competitive edge in character-level LLMing, particularly when combined with advanced regularization techniques like variational dropout.
• Efficiency and Scalability: Despite lacking non-linear recurrent depth, a feature often leveraged by conventional models to gain flexibility, mLSTM achieves notable performance, simplifying model construction and enhancing parallelizability.

Theoretical Implications

The integration of LSTM with multiplicative RNNs in mLSTM introduces a new paradigm within RNN architectures, addressing inherent limitations related to recovering from sequence modeling errors—a common bottleneck in vanilla and Long Short-Term Memory RNNs. The input-dependent transition feature potentially offers a pathway towards more robust sequence modeling abilities that could mitigate the vanishing gradient problem, often encountered in deep neural networks.

Practical Implications and Future Directions

Practically, mLSTM holds promise for tasks requiring autoregressive models capable of long-range dependencies, evident in NLP, speech processing, and beyond. Furthermore, its comparative performance with word-level models suggests potential applications in scenarios where vocabulary flexibility poses a challenge, such as rare word processing or modeling unstructured textual data.

Looking ahead, extending mLSTM's applicability to word-level LLMing and investigating its competency in handling continuous or non-sparse inputs could provide deeper insights. Additionally, exploring modifications or enhancements like dynamic evaluation or further integration with regularization strategies might optimize its capabilities and extend its practical utility across broader AI domains.

Conclusion

mLSTM emerges as a compelling advancement in RNN research, merging multiplicative transitions with LSTM's gating mechanisms to overcome traditional hurdles in sequence modeling. By emphasizing input-dependent transitions, mLSTM not only expands the horizon for RNN architecture development but also sets a benchmark for future studies intending to refine expressiveness and adaptability in neural sequence models. The work lays a foundation for further advances towards more intelligent and adaptive sequence modeling systems.