Context Tuning for In-Context Optimization

• URL: http://arxiv.org/abs/2507.04221v1
• Date: Sun, 06 Jul 2025 03:23:53 GMT
• Title: Context Tuning for In-Context Optimization
• Authors: Jack Lu, Ryan Teehan, Zhenbang Yang, Mengye Ren,
• Abstract summary: Context Tuning is a simple and effective method to enhance few-shot adaptation of language models (LLMs) without fine-tuning model parameters.<n>In contrast to prompt-based adaptation, Context Tuning initializes the trainable prompt or prefix with task-specific demonstration examples.<n>Extensive evaluations on benchmarks such as CrossFit, UnifiedQA, MMLU, BIG-Bench Hard, and ARC demonstrate that Context Tuning outperforms traditional prompt-based adaptation methods.
• Score: 11.728105991946773
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce Context Tuning, a simple and effective method to significantly enhance few-shot adaptation of language models (LLMs) without fine-tuning model parameters. While prompt-based adaptation techniques have demonstrated the effectiveness of lightweight adaptation methods for large language models (LLMs), they typically initialize a trainable prompt or prefix with irrelevant tokens for the task at hand. In contrast, Context Tuning initializes the trainable prompt or prefix with task-specific demonstration examples, leveraging the model's inherent In-Context Learning (ICL) ability to extract relevant information for improved few-shot learning performance. Extensive evaluations on benchmarks such as CrossFit, UnifiedQA, MMLU, BIG-Bench Hard, and ARC demonstrate that Context Tuning outperforms traditional prompt-based adaptation methods and achieves competitive accuracy to Test-Time Training with significantly higher training efficiency.

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