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

ArXiv ID: 2412.15298 “View on arXiv”

Authors: Unknown

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.

Keywords: Large Language Models (LLM), Prompt Optimization, DSPy, Hallucination Detection, Machine Learning

Complexity vs Empirical Score

  • Math Complexity: 4.0/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Street Traders
  • Why: The paper focuses on the practical application and comparison of existing prompt optimization algorithms within the DSPy framework, relying heavily on benchmark dataset implementation and performance metrics rather than novel mathematical derivations. The rigor is demonstrated through systematic experimentation on a public dataset (HaluBench) and comparative analysis, though it lacks the deep theoretical derivations typical of high-math finance research.
  flowchart TD
    A["Research Goal<br>Align LLM evaluation prompts<br>to human annotations"] --> B["Input: Hallucination Benchmark Dataset<br>with Ground Truth Labels"]
    B --> C["Methodology: DSPy Teleprompter Optimization<br>Compare 5 Algorithms"]
    
    C --> D{"Computational Process<br>Optimize LLM Judge Prompts"}
    D --> E["COPRO"]
    D --> F["MIPRO"]
    D --> G["BootstrapFewShot"]
    D --> H["BootstrapFewShot+Optuna"]
    D --> I["KNN Few Shot"]
    
    E --> J["Key Findings<br>Optimized prompts outperform benchmarks<br>Teleprompters differ in alignment effectiveness"]
    F --> J
    G --> J
    H --> J
    I --> J