Reflective Prompt Tuning through Language Model Function-Calling
Reflective Prompt Tuning (RPT) is a framework that uses LLM function calling to simulate the iterative workflow of human prompt engineers. RPT employs a diagnostic function to evaluate the target model over an optimization set, summarizes recurring failure modes, and uses accumulated memory to revise prompts iteratively. Across three reasoning tasks, RPT improves over initial prompts by up to 12.9 points, remains competitive with state of the art, and improves confidence calibration.
[2605.21781] Reflective Prompt Tuning through Language Model Function-Calling
[Submitted on 20 May 2026]
Title:Reflective Prompt Tuning through Language Model Function-Calling
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Abstract:Large language models (LLMs) have become increasingly capable of following instructions and complex reasoning, making prompting a flexible interface for adapting models without parameter updates. Yet prompt design remains labor-intensive and highly sensitive to formatting, phrasing, and instruction order, motivating automated prompt optimization methods that reduce manual effort while preserving inference-time flexibility. However, existing methods often search over prompt candidates or use fixed critique-refine pipelines driven by individual examples or small batches, limiting their ability to capture systematic error patterns and make targeted edits grounded in failure history. We propose Reflective Prompt Tuning (RPT), a framework that uses LLM function calling to simulate the iterative workflow of human prompt engineers. An LLM optimizer calls a diagnostic function that evaluates the target model over an entire optimization set, summarizes recurring failure modes, and returns a structured diagnostic report. The optimizer uses this report, together with an accumulated memory of prior reports, to revise the prompt for the next iteration. RPT further supports confidence-aware optimization by using calibration signals in diagnostic feedback and final prompt selection. Across three reasoning tasks, RPT improves over initial prompts by up to 12.9 points, remains competitive with state of the art, and improves confidence calibration. Our analyses show that RPT is especially effective on multi-hop and mathematical reasoning, producing targeted prompt revisions that align with diagnosed failure patterns and lead to gains in task performance and calibration.
Comments: 17 pages, 6 figures
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2605.21781 [cs.CL]
(or arXiv:2605.21781v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.21781
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Farima Fatahi Bayat [view email] [v1] Wed, 20 May 2026 22:21:29 UTC (509 KB)
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