# The Frequency Confound in Language-Model Surprisal and Metaphor Novelty

arXiv: https://arxiv.org/abs/2605.06506 · HTML: https://mlsanity.com/paper/2605.06506/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 7 May 2026.

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

- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling](https://mlsanity.com/paper/2304.01373/) (2023)
- [Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?](https://mlsanity.com/paper/2212.12131/) (2022)
- [Transformer-Based Language Model Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens](https://mlsanity.com/paper/2304.11389/) (2023)
- [Frequency Explains the Inverse Correlation of Large Language Models' Size, Training Data Amount, and Surprisal's Fit to Reading Times](https://mlsanity.com/paper/2402.02255/) (2024)
- [How to Compute the Probability of a Word](https://mlsanity.com/paper/2406.14561/) (2024)
- [On the Role of Context in Reading Time Prediction](https://mlsanity.com/paper/2409.08160/) (2024)
- [Surprisal and Metaphor Novelty Judgments: Moderate Correlations and Divergent Scaling Effects Revealed by Corpus-Based and Synthetic Datasets](https://mlsanity.com/paper/2601.02015/) (2026)

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

- [Revisiting the Estimation of Unigram Surprisal in Computational Psycholinguistics](https://mlsanity.com/paper/2610.18414/) (2026)

## Abstract (arXiv)

Language-model (LM) surprisal is widely used as a proxy for contextual predictability and has been reported to correlate with metaphor novelty judgments. However, surprisal is tightly intertwined with lexical frequency. We explore this interaction on metaphor novelty ratings using two different word frequency measures. We analyse surprisal estimates from eight Pythia model sizes and 154 training checkpoints. Across settings, word frequency is a stronger predictor of metaphor novelty than surprisal. Across training stages, the surprisal--novelty association peaks at an early stage and then falls again, mirroring a similarly timed increase in the surprisal--frequency association. These results suggest that the often-reported optimal LM surprisal settings may incorrectly associate contextual predictability with metaphor novelty and processing difficulty, whereas lexical frequency may be the major underlying factor.