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Efficient Alignment of Large Language Models via Data Sampling

Amrit Khera, Rajat Ghosh, Debojyoti Dutta

TL;DR

The paper addresses the high data and compute costs of aligning large language models by analyzing how alignment performance scales with data. It reveals an exponential plateau in performance and proposes an information-theoretic subsampling approach, ISA, built on a two-component Gaussian Mixture Model and entropy-based sampling to select a small, high-quality subset. Across multiple alignment datasets and models, ISA outperforms baselines and matches full-data performance using as little as 3.5–10% of the data, yielding substantial resource savings. This work offers a practical pathway to more scalable, cost-effective LLM alignment and lays groundwork for applying data-efficient alignment to even larger models.

Abstract

LLM alignment ensures that large language models behave safely and effectively by aligning their outputs with human values, goals, and intentions. Aligning LLMs employ huge amounts of data, computation, and time. Moreover, curating data with human feedback is expensive and takes time. Recent research depicts the benefit of data engineering in the fine-tuning and pre-training paradigms to bring down such costs. However, alignment differs from the afore-mentioned paradigms and it is unclear if data efficient alignment is feasible. In this work, we first aim to understand how the performance of LLM alignment scales with data. We find out that LLM alignment performance follows an exponential plateau pattern which tapers off post a rapid initial increase. Based on this, we identify data subsampling as a viable method to reduce resources required for alignment. Further, we propose an information theory-based methodology for efficient alignment by identifying a small high quality subset thereby reducing the computation and time required by alignment. We evaluate the proposed methodology over multiple datasets and compare the results. We find that the model aligned using our proposed methodology outperforms other sampling methods and performs comparable to the model aligned with the full dataset while using less than 10% data, leading to greater than 90% savings in costs, resources, and faster LLM alignment.

Efficient Alignment of Large Language Models via Data Sampling

TL;DR

The paper addresses the high data and compute costs of aligning large language models by analyzing how alignment performance scales with data. It reveals an exponential plateau in performance and proposes an information-theoretic subsampling approach, ISA, built on a two-component Gaussian Mixture Model and entropy-based sampling to select a small, high-quality subset. Across multiple alignment datasets and models, ISA outperforms baselines and matches full-data performance using as little as 3.5–10% of the data, yielding substantial resource savings. This work offers a practical pathway to more scalable, cost-effective LLM alignment and lays groundwork for applying data-efficient alignment to even larger models.

Abstract

LLM alignment ensures that large language models behave safely and effectively by aligning their outputs with human values, goals, and intentions. Aligning LLMs employ huge amounts of data, computation, and time. Moreover, curating data with human feedback is expensive and takes time. Recent research depicts the benefit of data engineering in the fine-tuning and pre-training paradigms to bring down such costs. However, alignment differs from the afore-mentioned paradigms and it is unclear if data efficient alignment is feasible. In this work, we first aim to understand how the performance of LLM alignment scales with data. We find out that LLM alignment performance follows an exponential plateau pattern which tapers off post a rapid initial increase. Based on this, we identify data subsampling as a viable method to reduce resources required for alignment. Further, we propose an information theory-based methodology for efficient alignment by identifying a small high quality subset thereby reducing the computation and time required by alignment. We evaluate the proposed methodology over multiple datasets and compare the results. We find that the model aligned using our proposed methodology outperforms other sampling methods and performs comparable to the model aligned with the full dataset while using less than 10% data, leading to greater than 90% savings in costs, resources, and faster LLM alignment.

Paper Structure

This paper contains 19 sections, 10 equations, 6 figures, 10 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Contributions
  3. Experiment Design
  4. Characteristics of Alignment Performance vs Data Sample Size (RQ-1)
  5. Optimal Sample Selection for Efficient Alignment (RQ-2)
  6. Conclusion
  7. Appendix / supplemental material
  8. Alignment Preliminary
  9. Related Works
  10. Alignment Performance Characteristic Curve Fitting
  11. Sampling Strategies
  12. Proposed Methodology: Information Sampling for Alignment (ISA)
  13. Density Sampling
  14. LLM Sampling
  15. LLama-30B Experiments
  16. ...and 4 more sections

Figures (6)

  • Figure 1: Alignment data performance with Mistral-7B-v0.1. Alignment performance follows an exponential plateau pattern captured by the proposed scaling law represented by the solid lines.
  • Figure 2: GPT-4o winrates for sampling strategies against alignment on all the dataset for Left. Anthropic HH Golden Dataset. Center. OpenAssistant Dataset Right. Ultrafeedback-Binarized Dataset
  • Figure 3: Information Sampling for Alignment
  • Figure 4: Density Sampling
  • Figure 5: LLM Sampling Prompt Creation
  • ...and 1 more figures