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Stateful Memory-Augmented Transformers for Efficient Dialogue Modeling
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Transformer encoder-decoder models have achieved great performance in dialogue generation tasks, however, their inability to process long dialogue history often leads to truncation of the context To address this problem, we propose a novel memory-augmented transformer that is compatible with existing pre-trained encoder-decoder models and enables efficient preservation of the dialogue history information. By incorporating a separate memory module alongside the pre-trained transformer, the model can effectively interchange information between the memory states and the current input context. We evaluate our model on three dialogue datasets and two language modeling datasets. Experimental results show that our method has achieved superior efficiency and performance compared to other pre-trained Transformer baselines.
Forward citations
Cited by 2 Pith papers
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Evaluating the Sensitivity of LLMs to Prior Context
Prior conversational context, especially from a different knowledge domain, can sharply reduce LLM multiple-choice accuracy, and repeating the task near the query mitigates the drop.
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Memory-Augmented Transformers: A Systematic Review from Neuroscience Principles to Enhanced Model Architectures
Memory-augmented Transformer research is organized into a three-axis taxonomy bridging neuroscience memory concepts to network designs, but no new result is produced.
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