arXiv:2605.23180v1 Announce Type: cross Abstract: We propose to improve in-context learning (ICL) by optimizing the continuous embeddings of a fixed few-shot prompt at test time. The key observation is that the log-probabilities a model assigns to its demonstrated outputs$\unicode{x2013}$available from a single forward pass without generating any tokens$\unicode{x2013}$provide a meaningful signal for how well the model has inferred the task from its demonstrations. We formalize this signal as a bounded, self-supervised confidence proxy and maximize it via zeroth-order optimization over the pro
The rapid advancement in large language models has exposed the limitations of static in-context learning, creating a demand for more dynamic and self-improving mechanisms to enhance model performance without extensive retraining.
This development proposes a method for AI models to adapt and optimize their understanding of tasks at test time, significantly improving efficiency and performance in current LLM applications and reducing the need for continuous fine-tuning.
AI models can now dynamically refine their few-shot prompts based on internal confidence signals, leading to more robust and adaptable in-context learning without requiring additional data generation or token processing.
- · AI developers
- · LLM applications
- · Makers of AI infrastructure
- · Inefficient prompt engineering methods
- · Static AI systems
Self-improving in-context learning leads to more accurate and reliable AI outputs.
This methodology could reduce the computational resources and human effort required for deploying and maintaining high-performance LLMs.
It might accelerate the development of more autonomous AI agents capable of continuous self-optimization in complex environments.
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Read at arXiv cs.LG