Stratified Prediction-Powered Inference for Hybrid Language Model Evaluation (2406.04291v2)

Abstract: Prediction-powered inference (PPI) is a method that improves statistical estimates based on limited human-labeled data. PPI achieves this by combining small amounts of human-labeled data with larger amounts of data labeled by a reasonably accurate -- but potentially biased -- automatic system, in a way that results in tighter confidence intervals for certain parameters of interest (e.g., the mean performance of a LLM). In this paper, we propose a method called Stratified Prediction-Powered Inference (StratPPI), in which we show that the basic PPI estimates can be considerably improved by employing simple data stratification strategies. Without making any assumptions on the underlying automatic labeling system or data distribution, we derive an algorithm for computing provably valid confidence intervals for population parameters (such as averages) that is based on stratified sampling. In particular, we show both theoretically and empirically that, with appropriate choices of stratification and sample allocation, our approach can provide substantially tighter confidence intervals than unstratified approaches. Specifically, StratPPI is expected to improve in cases where the performance of the autorater varies across different conditional distributions of the target data.

• The paper introduces StratPPI—a method that integrates stratified sampling with prediction-powered inference for more reliable hybrid model evaluation.
• It derives an optimal sample allocation strategy that reduces variance and produces significantly tighter confidence intervals than traditional methods.
• Empirical validation on synthetic and real-world datasets demonstrates its effectiveness across various applications, including multilingual summarization and image classification.

Stratified Prediction-Powered Inference for Hybrid LLM Evaluation

The paper "Stratified Prediction-Powered Inference for Hybrid LLM Evaluation" introduces an advanced method called Stratified Prediction-Powered Inference (StratPPI). This novel approach enhances conventional Prediction-Powered Inference (PPI) by integrating stratification techniques into the evaluation framework for hybrid models. The authors argue that the conventional evaluation methods, heavy on human-labeled data but light on automation, are often cost-prohibitive and inefficient when assessing LLMs. StratPPI aims to overcome these limitations by leveraging both small, high-quality human-labeled datasets and large datasets labeled by an automatic system known as an autorater.

Core Contributions

1. Stratified Sampling in PPI:
   • By stratifying the data based on conditional distributions of the target data, StratPPI provides improved performance estimations.
   • The approach creates different strata, each with distinct characteristics, enabling a more nuanced and reliable estimation.
2. Theoretical and Empirical Validation:
   • The authors derive an algorithm that uses stratified sampling to compute guaranteed valid confidence intervals for population parameters.
   • Both theoretical analysis and empirical results confirm that StratPPI yields substantially tighter confidence intervals compared to unstratified methods, particularly when the autorater's performance varies across strata.
3. Optimal Sample Allocation:
   • StratPPI includes a mechanism to determine optimal sample sizes for each stratum. This allocation optimally reduces the overall estimation variance.
   • The iterative process and tuning parameters help fine-tune the contributions of each stratum to the overall estimation.

Methodological Framework

The core innovation is the merging of stratified sampling with PPI. Here's how it works:

• Strata Definition: The input space is partitioned into non-overlapping strata. Each stratum represents a different subset of data with unique characteristics and distribution properties.
• Confidence Intervals:
  • Using the samples labeled by both humans and autoraters, StratPPI computes the bias of the autorater within each stratum.
  • The bias-corrected autorater estimates are then aggregated across strata to form tighter confidence intervals for the parameter of interest.
• Weighted M-Estimation:
  • The stratified estimates are computed via weighted M-estimators, ensuring the regularity conditions for statistical consistency.

Experimental Validation

The authors validate StratPPI using both synthetic and real-world datasets. The experiments are particularly focused on 1-D mean estimation:

• Synthetic Data: Simulation on synthetic datasets showed that StratPPI outperforms both classical inference using only human labels and the baseline PPI across various scenarios.
• Real Data:
  • Seahorse Dataset: Evaluates multilingual summarization tasks.
  • AttributedQA Dataset: Focuses on QA systems with retrieval-based support.
  • Galaxy Dataset: Extends the method's applicability beyond LLMs to image classification, specifically for classifying galaxies.

Implications and Future Directions

Theoretical Implications:

• StratPPI bridges a gap in hybrid model evaluations where the variability in autorater performance across different data segments is non-trivial.
• By incorporating stratified sampling, it controls for heterogeneity within data, leading to fewer required human labels for reliable confidence intervals.

Practical Implications:

• StratPPI offers immediate practical benefits for deploying and iterating upon LLMs in production environments. It helps reduce human labeling costs without compromising the reliability of performance estimates.

Future Directions:

• Extending the framework to multi-dimensional parameter estimation is a natural next step. This could involve more complex stratification schemas and enhanced computational techniques to handle larger and more varied datasets.
• Investigating the effectiveness of different stratification strategies based on model-specific characteristics could yield more globally optimal solutions.

Conclusion

StratPPI represents a significant methodological advancement in the evaluation of LLMs by combining stratified sampling with the strengths of Prediction-Powered Inference. By demonstrating both theoretical soundness and empirical robustness, this method provides a scalable solution to the growing challenge of efficient and reliable model evaluation in the era of expansive AI capabilities.