Call for Rigor in Reporting Quality of Instruction Tuning Data

2025-03-04

AI Summary

Current instruction tuning (IT) data quality evaluation relies predominantly on downstream LLM performance as an indirect proxy, but suffers from methodological flaws: nonstandardized and unreproducible hyperparameter configurations lead to contradictory quality judgments for identical datasets across different settings, severely undermining evaluation reliability. Method: This work systematically identifies nonstandardized hyperparameters as the primary source of data quality distortion in IT evaluation and advocates—first in the literature—that assessments must be anchored to fixed, reproducible training protocols. Using subsets of LIMA and Alpaca, we conduct controlled ablation experiments varying learning rate and batch size, and evaluate alignment performance via standardized LLM benchmarks. Contribution/Results: Empirical results demonstrate that altering only one hyperparameter (e.g., learning rate or batch size) can reverse relative data quality rankings. This study establishes a methodological benchmark for IT data evaluation and shifts the paradigm from experience-driven to protocol-driven assessment.

Technology Category

• Machine Learning: Auto ML and Hyperparameter Tuning
• Natural Language Processing: Safety and Robustness
• Search and Optimization: Algorithm Configuration

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

Abstract

Instruction tuning is crucial for adapting large language models (LLMs) to align with user intentions. Numerous studies emphasize the significance of the quality of instruction tuning (IT) data, revealing a strong correlation between IT data quality and the alignment performance of LLMs. In these studies, the quality of IT data is typically assessed by evaluating the performance of LLMs trained with that data. However, we identified a prevalent issue in such practice: hyperparameters for training models are often selected arbitrarily without adequate justification. We observed significant variations in hyperparameters applied across different studies, even when training the same model with the same data. In this study, we demonstrate the potential problems arising from this practice and emphasize the need for careful consideration in verifying data quality. Through our experiments on the quality of LIMA data and a selected set of 1,000 Alpaca data points, we demonstrate that arbitrary hyperparameter decisions can make any arbitrary conclusion.

Problem

• Arbitrary hyperparameter selection affects LLM performance evaluation.
• Lack of rigor in verifying instruction tuning data quality.
• Inconsistent hyperparameters lead to unreliable model alignment conclusions.

Innovation

• Emphasizes rigorous hyperparameter selection in training
• Highlights variability in hyperparameters across studies
• Demonstrates impact of hyperparameters on data quality conclusions