Summary of Online Adaptation Of Language Models with a Memory Of Amortized Contexts, by Jihoon Tack et al.
Online Adaptation of Language Models with a Memory of Amortized Contexts
by Jihoon Tack, Jaehyung Kim, Eric Mitchell, Jinwoo Shin, Yee Whye Teh, Jonathan Richard Schwarz
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Memory of Amortized Contexts (MAC) framework is an efficient and effective online adaptation method for large language models (LLMs). MAC enables the retention of strong knowledge from new documents, which are compressed into compact modulations stored in a memory bank. The model attends to relevant knowledge during question-answering tasks by extracting informative modulations. Amortization-based meta-learning reduces optimization requirements, allowing the frozen LLM to adapt during test time without further gradient updates. MAC outperforms alternatives like retrieval-augmented generations (RAGs) in terms of online adaptation performance, time, and memory efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models quickly become outdated due to rapid information generation and dissemination. To keep them updated, online learning is crucial for real-world applications. However, adapting large language models efficiently remains a challenge. MAC is an efficient online adaptation framework that retains strong knowledge from new documents by compressing and storing them in a memory bank. The model learns to extract relevant knowledge during question-answering tasks. Amortization-based meta-learning reduces optimization requirements, allowing the frozen LLM to adapt during test time. |
Keywords
* Artificial intelligence * Meta learning * Online learning * Optimization * Question answering
