# Zero and Few-shot Learning for Author Profiling

arXiv: https://arxiv.org/abs/2204.10543 · HTML: https://mlsanity.com/paper/2204.10543/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 37 references that are in the map.
- First posted on arXiv 22 Apr 2022.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [Adversarial NLI: A New Benchmark for Natural Language Understanding](https://mlsanity.com/paper/1910.14599/) (2019)
- [Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach](https://mlsanity.com/paper/1909.00161/) (2019)
- [Efficient Natural Language Response Suggestion for Smart Reply](https://mlsanity.com/paper/1705.00652/) (2017)
- [Entailment as Few-Shot Learner](https://mlsanity.com/paper/2104.14690/) (2021)
- [Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Start](https://mlsanity.com/paper/2010.02584/) (2020)
- [Few-Shot Learning with Siamese Networks and Label Tuning](https://mlsanity.com/paper/2203.14655/) (2022)
- [An Emotional Analysis of False Information in Social Media and News Articles](https://mlsanity.com/paper/1908.09951/) (2019)
- [Emotion Ratings: How Intensity, Annotation Confidence and Agreements are Entangled](https://mlsanity.com/paper/2103.01667/) (2021)
- [Unsupervised Label Refinement Improves Dataless Text Classification](https://mlsanity.com/paper/2012.04194/) (2020)
- [A Low Dimensionality Representation for Language Variety Identification](https://mlsanity.com/paper/1705.10754/) (2017)

## Built on by (most cited first, 4 of 4)

- [PART: Pre-trained Authorship Representation Transformer](https://mlsanity.com/paper/2209.15373/) (2022)
- [Adversarial Robustness of Prompt-based Few-Shot Learning for Natural Language Understanding](https://mlsanity.com/paper/2306.11066/) (2023)
- [Prompt-based Personality Profiling: Reinforcement Learning for Relevance Filtering](https://mlsanity.com/paper/2409.04122/) (2024)
- [A Computational Framework to Identify Self-Aspects in Text](https://mlsanity.com/paper/2507.13115/) (2025)

## Abstract (arXiv)

Author profiling classifies author characteristics by analyzing how language is shared among people. In this work, we study that task from a low-resource viewpoint: using little or no training data. We explore different zero and few-shot models based on entailment and evaluate our systems on several profiling tasks in Spanish and English. In addition, we study the effect of both the entailment hypothesis and the size of the few-shot training sample. We find that entailment-based models out-perform supervised text classifiers based on roberta-XLM and that we can reach 80% of the accuracy of previous approaches using less than 50% of the training data on average.