# Generate labeled training data using Prompt Programming and GPT-3. An example of Big Five Personality Classification

arXiv: https://arxiv.org/abs/2303.12279 · HTML: https://mlsanity.com/paper/2303.12279/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 22 Mar 2023.

## Builds on (most cited first, 8 of 8)

- [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)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://mlsanity.com/paper/2102.07350/) (2021)
- [AdapterHub: A Framework for Adapting Transformers](https://mlsanity.com/paper/2007.07779/) (2020)
- [MultiWOZ 2.2 : A Dialogue Dataset with Additional Annotation Corrections and State Tracking Baselines](https://mlsanity.com/paper/2007.12720/) (2020)
- [TimeLMs: Diachronic Language Models from Twitter](https://mlsanity.com/paper/2202.03829/) (2022)

## Built on by (most cited first, 1 of 1)

- [PersonaTAB: Predicting Personality Traits using Textual, Acoustic, and Behavioral Cues in Fully-Duplex Speech Dialogs](https://mlsanity.com/paper/2505.14356/) (2025)

## Abstract (arXiv)

We generated 25000 conversations labeled with Big Five Personality traits using prompt programming at GPT-3. Then we train Big Five classification models with these data and evaluate them with 2500 data from generated dialogues and real conversational datasets labeled in Big Five by human annotators. The results indicated that this approach is promising for creating effective training data. We then compare the performance by different training approaches and models. Our results suggest that using Adapter-Transformers and transfer learning from pre-trained RoBERTa sentiment analysis model will perform best with the generated data. Our best model obtained an accuracy of 0.71 in generated data and 0.65 in real datasets. Finally, we discuss this approach's potential limitations and confidence metric.