MEND: Meta Demonstration Distillation for Efficient and Effective In-Context Learning
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Keywords: in-context learning, language modeling, data distillation, knowledge distillation
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TL;DR: efficient and effect demonstration distillation for large language model in in-context learning
Abstract: Large Language models (LLMs) have demonstrated impressive in-context learning (ICL) capabilities,
where a LLM makes predictions for a given test input together with a few input-output pairs (demonstrations).
Nevertheless, the inclusion of demonstrations poses a challenge, leading to a quadratic increase in the computational overhead of the self-attention mechanism.
Existing solutions attempt to condense lengthy demonstrations into compact vectors.
However, they often require task-specific retraining or compromise LLM's in-context learning performance.
To mitigate these challenges, we present Meta Demonstration Distillation (MEND), where a language model learns to distill any lengthy demonstrations into vectors without retraining for a new downstream task.
We exploit the knowledge distillation to enhance alignment between MEND and MEND, achieving both efficiency and effectiveness concurrently.
MEND is endowed with the meta-knowledge of distilling demonstrations through a two-stage training process, which includes meta-distillation pretraining and fine-tuning.
Comprehensive evaluations across seven diverse ICL settings using decoder-only (GPT-2) and encoder-decoder (T5) attest to MEND's prowess.
It not only matches but often outperforms the Vanilla ICL as well as other state-of-the-art distillation models, while significantly reducing the computational demands.
This innovation promises enhanced scalability and efficiency for the practical deployment of large language models.
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Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Submission Number: 2742
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