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Prompt optimizers show regressions in unscored requirements, study finds

By PulseAugur Editorial · [1 sources] ·

A preregistered study examined prompt optimizers like GEPA, MIPROv2, and TextGrad, finding that while they improve target metrics on datasets such as HotpotQA, they also introduce regressions in unscored requirements like output format and citation validity. Specifically, under a 200-token output cap, a significant percentage of responses were truncated before providing an answer. However, with a more lenient 1,024-token cap, these extraction failures were eliminated, and performance on unscored requirements improved. AI

IMPACT Highlights potential trade-offs in prompt optimization, suggesting careful consideration of output constraints and evaluation metrics.

RANK_REASON The cluster contains an academic paper detailing a study on prompt optimization techniques. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Prompt optimizers show regressions in unscored requirements, study finds

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The cluster contains an academic paper detailing a study on prompt optimization techniques. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Sangdo Han, Moonsoo Kim ·

    Do Prompt Optimizers Break Requirements They Are Not Scored On? A Preregistered Study

    arXiv:2610.13287v1 Announce Type: new Abstract: Prompt optimizers rewrite a prompt to raise one target metric, but prompts also state requirements the metric does not score, such as an output format, citations, or abstention. In a preregistered study, we run GEPA, MIPROv2, and Te…

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