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Ordered Semantically Diverse Sampling for Textual Data

Ashish Tiwari, Mukul Singh, Ananya Singha, Arjun Radhakrishna

TL;DR

The paper addresses the problem of diversity sampling for textual data by proposing an ordered, model- and task-agnostic approach governed by the aggregated wasted opportunity metric. It introduces a PCA-based principled sampler that embeds texts, reduces dimensionality to $n$ components, and outputs an ordered $3n$-sample sequence, enabling efficient, diverse coverage. Across 22 text-classification benchmarks transformed into diversity tasks, the proposed method consistently outperforms baselines by large margins (up to 61%) and offers favorable runtimes, with ablations validating the contribution of each component. The approach leverages a coreset-like perspective and is applicable to large-language-model workflows where small, diverse samples can drive effective downstream analysis or prompting. Overall, this work provides a concrete, scalable framework for ordered diverse sampling in textual data with practical impact on model-agnostic evaluation and data-efficiency scenarios.

Abstract

The goal of diversity sampling is to select a representative subset of data in a way that maximizes information contained in the subset while keeping its cardinality small. We introduce the ordered diverse sampling problem based on a new metric that measures the diversity in an ordered list of samples. We present a novel approach for generating ordered diverse samples for textual data that uses principal components on the embedding vectors. The proposed approach is simple and compared with existing approaches using the new metric. We transform standard text classification benchmarks into benchmarks for ordered diverse sampling. Our empirical evaluation shows that prevailing approaches perform 6% to 61% worse than our method while also being more time inefficient. Ablation studies show how the parts of the new approach contribute to the overall metrics.

Ordered Semantically Diverse Sampling for Textual Data

TL;DR

The paper addresses the problem of diversity sampling for textual data by proposing an ordered, model- and task-agnostic approach governed by the aggregated wasted opportunity metric. It introduces a PCA-based principled sampler that embeds texts, reduces dimensionality to components, and outputs an ordered -sample sequence, enabling efficient, diverse coverage. Across 22 text-classification benchmarks transformed into diversity tasks, the proposed method consistently outperforms baselines by large margins (up to 61%) and offers favorable runtimes, with ablations validating the contribution of each component. The approach leverages a coreset-like perspective and is applicable to large-language-model workflows where small, diverse samples can drive effective downstream analysis or prompting. Overall, this work provides a concrete, scalable framework for ordered diverse sampling in textual data with practical impact on model-agnostic evaluation and data-efficiency scenarios.

Abstract

The goal of diversity sampling is to select a representative subset of data in a way that maximizes information contained in the subset while keeping its cardinality small. We introduce the ordered diverse sampling problem based on a new metric that measures the diversity in an ordered list of samples. We present a novel approach for generating ordered diverse samples for textual data that uses principal components on the embedding vectors. The proposed approach is simple and compared with existing approaches using the new metric. We transform standard text classification benchmarks into benchmarks for ordered diverse sampling. Our empirical evaluation shows that prevailing approaches perform 6% to 61% worse than our method while also being more time inefficient. Ablation studies show how the parts of the new approach contribute to the overall metrics.

Paper Structure

This paper contains 13 sections, 2 equations, 5 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Diversity Sampling Problem and Metric
  4. Benchmarks for Evaluating Diversity Samplers
  5. Principled Samplers
  6. Principled Sampler v2
  7. Evaluation
  8. Datasets
  9. Comparing with Baselines
  10. Effect of the Embedding Procedure
  11. Ablation Studies
  12. Time Comparison
  13. Conclusion

Figures (5)

  • Figure 1: Motivation for Diversity Sampling
  • Figure 2: Comparing with baselines using $3n=18$ samples using openai embeddings.
  • Figure 3: Comparing with baselines using $3n=18$ samples using tfidf embeddings.
  • Figure 4: Comparing Samplers v1 and v2 with openai embedding and with TFIDF-based embedding.
  • Figure 5: Time taken by different methods (a) to sample 60 from datasets of different sizes (b) to sample different number of items from a dataset with 2500 items.

Theorems & Definitions (7)

  • Example 1
  • Example 2
  • Example 3
  • Remark 1
  • Remark 2
  • Remark 3
  • Remark 4