Dataset Cartography for Large Language Model Alignment: Mapping and Diagnosing Preference Data

2025-05-29

AI Summary

High-cost, low-efficiency, and hard-to-diagnose human preference data collection hinders scalable alignment. This paper proposes Alignment Data Map: a novel two-dimensional data mapping paradigm (mean–variance) based on alignment score distributions, enabling fully automated, annotation-free quantification and regional filtering of preference data quality. Methodologically, we employ GPT-4o as a lightweight alignment proxy model to automatically assess LLM response quality, generate per-response alignment scores, and construct a statistical map to identify high-value samples—characterized by high mean alignment scores and low variance. Experiments show that fine-tuning on only 33% of the full dataset—specifically the top-quality subset—matches or surpasses performance achieved with the entire dataset. Moreover, the map reliably identifies low-impact and potentially mislabeled samples, validating its diagnostic capability. The core contribution is a principled data mapping paradigm that jointly enables efficient data acquisition and interpretable, post-hoc data diagnosis.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Humans and AI: Learning Human Values and Preferences
• Knowledge Representation and Reasoning: Preferences

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Human preference data plays a critical role in aligning large language models (LLMs) with human values. However, collecting such data is often expensive and inefficient, posing a significant scalability challenge. To address this, we introduce Alignment Data Map, a GPT-4o-assisted tool for analyzing and diagnosing preference data. Using GPT-4o as a proxy for LLM alignment, we compute alignment scores for LLM-generated responses to instructions from existing preference datasets. These scores are then used to construct an Alignment Data Map based on their mean and variance. Our experiments show that using only 33 percent of the data, specifically samples in the high-mean, low-variance region, achieves performance comparable to or better than using the entire dataset. This finding suggests that the Alignment Data Map can significantly improve data collection efficiency by identifying high-quality samples for LLM alignment without requiring explicit annotations. Moreover, the Alignment Data Map can diagnose existing preference datasets. Our analysis shows that it effectively detects low-impact or potentially misannotated samples. Source code is available online.

Problem

• Improving efficiency in collecting human preference data for LLM alignment
• Identifying high-quality samples without explicit annotations for alignment
• Diagnosing and detecting low-impact or misannotated samples in datasets

Innovation

• GPT-4o-assisted tool for preference data analysis
• Alignment scores based on mean and variance
• High-quality sample identification without annotations