# Revenge of the Fallen? Recurrent Models Match Transformers at Predicting Human Language Comprehension Metrics

arXiv: https://arxiv.org/abs/2404.19178 · HTML: https://mlsanity.com/paper/2404.19178/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 114 references that are in the map.
- First posted on arXiv 30 Apr 2024.

## Builds on (most cited first, 29 of 29)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [Pointer Sentinel Mixture Models](https://mlsanity.com/paper/1609.07843/) (2016)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling](https://mlsanity.com/paper/2304.01373/) (2023)
- [RWKV: Reinventing RNNs for the Transformer Era](https://mlsanity.com/paper/2305.13048/) (2023)
- [Eliciting Latent Predictions from Transformers with the Tuned Lens](https://mlsanity.com/paper/2303.08112/) (2023)
- [On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behavior](https://mlsanity.com/paper/2006.01912/) (2020)
- [Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?](https://mlsanity.com/paper/2212.12131/) (2022)
- [Testing the Predictions of Surprisal Theory in 11 Languages](https://mlsanity.com/paper/2307.03667/) (2023)
- [A Primer on Neural Network Models for Natural Language Processing](https://mlsanity.com/paper/1510.00726/) (2015)
- [Revisiting the Uniform Information Density Hypothesis](https://mlsanity.com/paper/2109.11635/) (2021)
- [Lower Perplexity is Not Always Human-Like](https://mlsanity.com/paper/2106.01229/) (2021)
- [Human Sentence Processing: Recurrence or Attention?](https://mlsanity.com/paper/2005.09471/) (2020)
- [Exploring BERT's Sensitivity to Lexical Cues using Tests from Semantic Priming](https://mlsanity.com/paper/2010.03010/) (2020)
- [Transformer-Based Language Model Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens](https://mlsanity.com/paper/2304.11389/) (2023)
- [The Natural Stories Corpus](https://mlsanity.com/paper/1708.05763/) (2017)
- [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)
- [Accounting for Agreement Phenomena in Sentence Comprehension with Transformer Language Models: Effects of Similarity-based Interference on Surprisal and Attention](https://mlsanity.com/paper/2104.12874/) (2021)
- [Probabilistic Predictions of People Perusing: Evaluating Metrics of Language Model Performance for Psycholinguistic Modeling](https://mlsanity.com/paper/2009.03954/) (2020)
- [So Cloze yet so Far: N400 Amplitude is Better Predicted by Distributional Information than Human Predictability Judgements](https://mlsanity.com/paper/2109.01226/) (2021)
- [Different kinds of cognitive plausibility: why are transformers better than RNNs at predicting N400 amplitude?](https://mlsanity.com/paper/2107.09648/) (2021)
- [When Language Models Fall in Love: Animacy Processing in Transformer Language Models](https://mlsanity.com/paper/2310.15004/) (2023)
- [Can Language Models Be Tricked by Language Illusions? Easier with Syntax, Harder with Semantics](https://mlsanity.com/paper/2311.01386/) (2023)
- [Collateral facilitation in humans and language models](https://mlsanity.com/paper/2211.05198/) (2022)

## Built on by (most cited first, 6 of 6)

- [Large Language Models Are Human-Like Internally](https://mlsanity.com/paper/2502.01615/) (2025)
- [A Survey of RWKV](https://mlsanity.com/paper/2412.14847/) (2024)
- [Signatures of human-like processing in Transformer forward passes](https://mlsanity.com/paper/2504.14107/) (2025)
- [N-gram-like Language Models Predict Naturalistic Reading Time Best](https://mlsanity.com/paper/2603.09872/) (2026)
- [LLM surprisal is necessary but not sufficient to capture English garden-path effects: Evidence from joint latent modeling of reading paradigms](https://mlsanity.com/paper/2602.04489/) (2026)
- [What a Cross-Model Fixed-Point Census Can and Cannot Arbitrate About Repetition](https://mlsanity.com/paper/2609.29507/) (2026)

## Abstract (arXiv)

Transformers have generally supplanted recurrent neural networks as the dominant architecture for both natural language processing tasks and for modelling the effect of predictability on online human language comprehension. However, two recently developed recurrent model architectures, RWKV and Mamba, appear to perform natural language tasks comparably to or better than transformers of equivalent scale. In this paper, we show that contemporary recurrent models are now also able to match - and in some cases, exceed - the performance of comparably sized transformers at modeling online human language comprehension. This suggests that transformer language models are not uniquely suited to this task, and opens up new directions for debates about the extent to which architectural features of language models make them better or worse models of human language comprehension.