# Exemplar Guided Active Learning

arXiv: https://arxiv.org/abs/2011.01285 · HTML: https://mlsanity.com/paper/2011.01285/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [A Survey on Deep Active Learning: Recent Advances and New Frontiers](https://mlsanity.com/paper/2405.00334/) (2024)
- [In Defense of Core-set: A Density-aware Core-set Selection for Active Learning](https://mlsanity.com/paper/2206.04838/) (2022)
- [LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet?](https://mlsanity.com/paper/2509.21403/) (2025)
- [AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced Datasets](https://mlsanity.com/paper/2404.05623/) (2024)
- [Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class Challenge](https://mlsanity.com/paper/2305.02459/) (2023)
- [The Speed-up Factor: A Quantitative Multi-Iteration Active Learning Performance Metric](https://mlsanity.com/paper/2602.13359/) (2026)

## Abstract (arXiv)

We consider the problem of wisely using a limited budget to label a small subset of a large unlabeled dataset. We are motivated by the NLP problem of word sense disambiguation. For any word, we have a set of candidate labels from a knowledge base, but the label set is not necessarily representative of what occurs in the data: there may exist labels in the knowledge base that very rarely occur in the corpus because the sense is rare in modern English; and conversely there may exist true labels that do not exist in our knowledge base. Our aim is to obtain a classifier that performs as well as possible on examples of each "common class" that occurs with frequency above a given threshold in the unlabeled set while annotating as few examples as possible from "rare classes" whose labels occur with less than this frequency. The challenge is that we are not informed which labels are common and which are rare, and the true label distribution may exhibit extreme skew. We describe an active learning approach that (1) explicitly searches for rare classes by leveraging the contextual embedding spaces provided by modern language models, and (2) incorporates a stopping rule that ignores classes once we prove that they occur below our target threshold with high probability. We prove that our algorithm only costs logarithmically more than a hypothetical approach that knows all true label frequencies and show experimentally that incorporating automated search can significantly reduce the number of samples needed to reach target accuracy levels.