MEND: Meta dEmonstratioN Distillation for Efficient and Effective In-Context Learning

• URL: http://arxiv.org/abs/2403.06914v2
• Date: Tue, 12 Mar 2024 15:52:14 GMT
• Title: MEND: Meta dEmonstratioN Distillation for Efficient and Effective In-Context Learning
• Authors: Yichuan Li, Xiyao Ma, Sixing Lu, Kyumin Lee, Xiaohu Liu, Chenlei Guo
• Abstract summary: Large Language models (LLMs) make predictions for a given test input together with a few input-output pairs (demonstrations) Existing solutions attempt to distill lengthy demonstrations into compact vectors. 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.
• Score: 9.271196993624944
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 leads to a quadratic increase in the computational overhead of the self-attention mechanism. Existing solutions attempt to distill 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 LLM, achieving both efficiency and effectiveness simultaneously. 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 task partitions 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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