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New Local Support Learning framework combats catastrophic forgetting in LLMs

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Local Support Learning (LSL), a novel framework designed to combat catastrophic forgetting in large pre-trained models. LSL employs a dual-component system: a standard weight adapter for new learning and a gating function that restricts updates to specific data distributions. This approach aims to retain prior capabilities without needing access to old data, proving effective in LLMs up to 7 billion parameters. AI

IMPACT This framework could enable more robust and continuous learning in large language models, reducing the need for complete retraining.

RANK_REASON The cluster contains a research paper detailing a new method for LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Local Support Learning framework combats catastrophic forgetting in LLMs

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The cluster contains a research paper detailing a new method for LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Local Support Learning

    We explore catastrophic forgetting in the context of large pre-trained models. By considering forgetting as a geometric problem in the input space of each weight matrix, we uncover a natural retention objective under which updates produced by gradient-based optimizers are subopti…

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