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A Comparative Study of DSPy Teleprompter Algorithms for Aligning Large Language Models Evaluation Metrics to Human Evaluation

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  • Bhaskarjit Sarmah
  • Kriti Dutta
  • Anna Grigoryan
  • Sachin Tiwari
  • Stefano Pasquali
  • Dhagash Mehta

Abstract

We argue that the Declarative Self-improving Python (DSPy) optimizers are a way to align the large language model (LLM) prompts and their evaluations to the human annotations. We present a comparative analysis of five teleprompter algorithms, namely, Cooperative Prompt Optimization (COPRO), Multi-Stage Instruction Prompt Optimization (MIPRO), BootstrapFewShot, BootstrapFewShot with Optuna, and K-Nearest Neighbor Few Shot, within the DSPy framework with respect to their ability to align with human evaluations. As a concrete example, we focus on optimizing the prompt to align hallucination detection (using LLM as a judge) to human annotated ground truth labels for a publicly available benchmark dataset. Our experiments demonstrate that optimized prompts can outperform various benchmark methods to detect hallucination, and certain telemprompters outperform the others in at least these experiments.

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  • Bhaskarjit Sarmah & Kriti Dutta & Anna Grigoryan & Sachin Tiwari & Stefano Pasquali & Dhagash Mehta, 2024. "A Comparative Study of DSPy Teleprompter Algorithms for Aligning Large Language Models Evaluation Metrics to Human Evaluation," Papers 2412.15298, arXiv.org.
    • Handle: RePEc:arx:papers:2412.15298
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    1. Bhaskarjit Sarmah & Mingshu Li & Jingrao Lyu & Sebastian Frank & Nathalia Castellanos & Stefano Pasquali & Dhagash Mehta, 2024. "How to Choose a Threshold for an Evaluation Metric for Large Language Models," Papers 2412.12148, arXiv.org.
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