MemoryPrompt: A Light Wrapper to Improve Context Tracking in Pre-trained Language Models

• URL: http://arxiv.org/abs/2402.15268v1
• Date: Fri, 23 Feb 2024 11:30:39 GMT
• Title: MemoryPrompt: A Light Wrapper to Improve Context Tracking in Pre-trained Language Models
• Authors: Nathana\"el Carraz Rakotonirina, Marco Baroni
• Abstract summary: Transformer-based language models (LMs) track contextual information through large, hard-coded input windows. We introduce MemoryPrompt, a leaner approach in which the LM is complemented by a small auxiliary recurrent network that passes information to the LM by prefixing its regular input with a sequence of vectors. tested on a task designed to probe a LM's ability to keep track of multiple fact updates, a MemoryPrompt-augmented LM outperforms much larger LMs that have access to the full input history.
• Score: 10.783764497590473
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transformer-based language models (LMs) track contextual information through large, hard-coded input windows. We introduce MemoryPrompt, a leaner approach in which the LM is complemented by a small auxiliary recurrent network that passes information to the LM by prefixing its regular input with a sequence of vectors, akin to soft prompts, without requiring LM finetuning. Tested on a task designed to probe a LM's ability to keep track of multiple fact updates, a MemoryPrompt-augmented LM outperforms much larger LMs that have access to the full input history. We also test MemoryPrompt on a long-distance dialogue dataset, where its performance is comparable to that of a model conditioned on the entire conversation history. In both experiments we also observe that, unlike full-finetuning approaches, MemoryPrompt does not suffer from catastrophic forgetting when adapted to new tasks, thus not disrupting the generalist capabilities of the underlying LM.

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