TATRA: Training-Free Instance-Adaptive Prompting Through Rephrasing and Aggregation

• URL: http://arxiv.org/abs/2603.03298v1
• Date: Fri, 06 Feb 2026 19:20:35 GMT
• Title: TATRA: Training-Free Instance-Adaptive Prompting Through Rephrasing and Aggregation
• Abstract summary: Large Language Models (LLMs) have improved substantially alignment, yet their behavior remains highly sensitive to prompt phrasing.<n>We introduce TATRA, a dataset-free prompting method that constructs instance-specific few-shot prompts.<n>On mathematical reasoning benchmarks, TATRA achieves state-of-the-art performance on GSM8K and DeepMath.
• Score: 10.52527481709651
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have improved substantially alignment, yet their behavior remains highly sensitive to prompt phrasing. This brittleness has motivated automated prompt engineering, but most existing methods (i) require a task-specific training set, (ii) rely on expensive iterative optimization to produce a single dataset-level prompt, and (iii) must be rerun from scratch for each new task. We introduce TATRA, a dataset-free prompting method that constructs instance-specific few-shot prompts by synthesizing on-the-fly examples to accompany a user-provided instruction. TATRA requires no labeled training data and avoids task-specific optimization loops, while retaining the benefits of demonstration-based prompting. Across standard text classification benchmarks, TATRA matches or improves over strong prompt-optimization baselines that depend on training data and extensive search. On mathematical reasoning benchmarks, TATRA achieves state-of-the-art performance on GSM8K and DeepMath, outperforming methods that explicitly optimize prompts on those tasks. Our results suggest that per-instance construction of effective in-context examples is more important than running long, expensive optimization loops to produce a single prompt per task. We will make all code publicly available upon acceptance of the paper. Code is available at https://github.com/BMD223/TATRA

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