# In your own words: computationally identifying interpretable themes in free-text survey data

arXiv: https://arxiv.org/abs/2603.26930 · HTML: https://mlsanity.com/paper/2603.26930/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 116 references that are in the map.
- First posted on arXiv 27 Mar 2026.

## Builds on (most cited first, 7 of 7)

- [Sparse Autoencoders Find Highly Interpretable Features in Language Models](https://mlsanity.com/paper/2309.08600/) (2023)
- [Toy Models of Superposition](https://mlsanity.com/paper/2209.10652/) (2022)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Scaling and evaluating sparse autoencoders](https://mlsanity.com/paper/2406.04093/) (2024)
- [Disentangling Dense Embeddings with Sparse Autoencoders](https://mlsanity.com/paper/2408.00657/) (2024)
- [Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit](https://mlsanity.com/paper/2512.10092/) (2025)
- [Interpretable Discovery from Unstructured Data: A High-Dimensional Approach](https://mlsanity.com/paper/2511.01680/) (2025)

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

- [The Measurement Revolution? Credible Measurement and Inference in the Age of AI](https://mlsanity.com/paper/2608.23524/) (2026)

## Abstract (arXiv)

Free-text survey responses can provide nuance often missed by structured questions, but remain difficult to statistically analyze. To address this, we introduce In Your Own Words, a computational framework for exploratory analyses of free-text survey data that identifies structured, interpretable themes in free-text responses, facilitating systematic analysis. To illustrate the benefits of this approach, we apply it to a new dataset of free-text descriptions of race, gender, and sexual orientation from 1,004 U.S. participants. The themes our approach produces on this dataset are more coherent and interpretable than those produced by past computational methods. The themes have three practical applications in survey research. First, they can suggest structured questions to add to future surveys by surfacing salient constructs - such as belonging and identity fluidity - that existing surveys do not capture. Second, the themes reveal heterogeneity within standardized categories, explaining additional variation in health, well-being, and identity importance. Third, the themes illuminate systematic discordance between self-identified and perceived identities, highlighting mechanisms of misrecognition that existing measures do not reflect. More broadly, our framework can be deployed in a wide range of survey settings to identify interpretable themes from free text, complementing existing qualitative methods.