# Deep Lexical Hypothesis: Identifying personality structure in natural language

arXiv: https://arxiv.org/abs/2203.02092 · HTML: https://mlsanity.com/paper/2203.02092/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 138 references that are in the map.
- First posted on arXiv 4 Mar 2022.

## Builds on (most cited first, 19 of 19)

- [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)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Scaling Language Models: Methods, Analysis & Insights from Training Gopher](https://mlsanity.com/paper/2112.11446/) (2021)
- [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015)
- [StereoSet: Measuring stereotypical bias in pretrained language models](https://mlsanity.com/paper/2004.09456/) (2020)
- [Deduplicating Training Data Makes Language Models Better](https://mlsanity.com/paper/2107.06499/) (2021)
- [Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM](https://mlsanity.com/paper/2104.04473/) (2021)
- [ClipCap: CLIP Prefix for Image Captioning](https://mlsanity.com/paper/2111.09734/) (2021)
- [Pre-Trained Models: Past, Present and Future](https://mlsanity.com/paper/2106.07139/) (2021)
- [How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models](https://mlsanity.com/paper/2012.15613/) (2020)
- [A Simple Method for Commonsense Reasoning](https://mlsanity.com/paper/1806.02847/) (2018)
- [Identifying and Reducing Gender Bias in Word-Level Language Models](https://mlsanity.com/paper/1904.03035/) (2019)
- [Machine Reading Comprehension: The Role of Contextualized Language Models and Beyond](https://mlsanity.com/paper/2005.06249/) (2020)
- [Varimax rotation based on gradient projection needs between 10 and more than 500 random start loading matrices for optimal performance](https://mlsanity.com/paper/1809.04885/) (2018)

## Built on by (most cited first, 10 of 10)

- [Self-assessment, Exhibition, and Recognition: a Review of Personality in Large Language Models](https://mlsanity.com/paper/2406.17624/) (2024)
- [Large language models can replicate cross-cultural differences in personality](https://mlsanity.com/paper/2310.10679/) (2023)
- [The MuSe 2024 Multimodal Sentiment Analysis Challenge: Social Perception and Humor Recognition](https://mlsanity.com/paper/2406.07753/) (2024)
- [Neural network embeddings recover value dimensions from psychometric survey items on par with human data](https://mlsanity.com/paper/2509.24906/) (2025)
- [PhDGPT: Introducing a psychometric and linguistic dataset about how large language models perceive graduate students and professors in psychology](https://mlsanity.com/paper/2411.10473/) (2024)
- [Personality Prediction from Life Stories using Language Models](https://mlsanity.com/paper/2506.19258/) (2025)
- [Measuring How LLMs Internalize Human Psychological Concepts: A preliminary analysis](https://mlsanity.com/paper/2506.23055/) (2025)
- [Machine learning methods fail to provide cohesive atheoretical construction of personality traits from semantic embeddings](https://mlsanity.com/paper/2510.09739/) (2025)
- [Natural Language Processing Psychometrics](https://mlsanity.com/paper/2608.07316/) (2026)
- [Implicit Personality Representations in Humans and LLMs](https://mlsanity.com/paper/2609.12704/) (2026)

## Abstract (arXiv)

Recent advances in natural language processing (NLP) have produced general models that can perform complex tasks such as summarizing long passages and translating across languages. Here, we introduce a method to extract adjective similarities from language models as done with survey-based ratings in traditional psycholexical studies but using millions of times more text in a natural setting. The correlational structure produced through this method is highly similar to that of self- and other-ratings of 435 terms reported by Saucier and Goldberg (1996a). The first three unrotated factors produced using NLP are congruent with those in survey data, with coefficients of 0.89, 0.79, and 0.79. This structure is robust to many modeling decisions: adjective set, including those with 1,710 terms (Goldberg, 1982) and 18,000 terms (Allport & Odbert, 1936); the query used to extract correlations; and language model. Notably, Neuroticism and Openness are only weakly and inconsistently recovered. This is a new source of signal that is closer to the original (semantic) vision of the Lexical Hypothesis. The method can be applied where surveys cannot: in dozens of languages simultaneously, with tens of thousands of items, on historical text, and at extremely large scale for little cost. The code is made public to facilitate reproduction and fast iteration in new directions of research.