Flexible Language Models Adapt with Memory and Amortization

Language models are powerful AI systems that can generate human-like text. But they often struggle to adapt to new tasks or domains - they tend to 'forget' what they've learned previously. This paper introduces a , rather than being limited to narrowly-defined tasks. This could lead to more flexible, capable, and useful language AI systems.

Key Findings

• The proposed approach, called "Online Adaptation of Language Models with a Memory of Amortized Contexts" (OAL-MAC), allows language models to quickly adapt to new tasks or domains.


• OAL-MAC uses a memory module to store relevant contextual information, enabling the model to 'remember' and apply its previous knowledge.


• The adaptation process is 'amortized' over time, making it more efficient and less disruptive to the model's existing knowledge.


• OAL-MAC outperforms standard fine-tuning approaches on several language tasks, demonstrating the benefits of the memory-based, amortized adaptation strategy.

Technical Explanation

The paper introduces a new approach called "Online Adaptation of Language Models with a Memory of Amortized Contexts" (OAL-MAC). The key components are:

1. Memory Module: OAL-MAC uses a memory module to store and retrieve relevant contextual information about previous tasks or domains. This allows the language model to 'remember' and apply its prior knowledge when faced with a new task.




2. Amortized Adaptation: Rather than adapting the entire language model at once, OAL-MAC spreads out the adaptation process over time. This 'amortizes' the computational cost and makes the adaptation less disruptive to the model's existing knowledge.




3. Adaptation Process: When presented with a new task, OAL-MAC first retrieves relevant context information from its memory module. It then uses this context to guide a targeted adaptation of the language model, rather than performing a complete fine-tuning.

The experiments show that OAL-MAC outperforms standard fine-tuning approaches on a variety of language tasks. This demonstrates the benefits of the memory-based, amortized adaptation strategy for enabling language models to quickly adapt to new situations while retaining their broader knowledge.

Implications for the Field

This research advances the state of the art in or following me on Twitter for more AI and machine learning content.