# What Makes Two Language Models Think Alike?

arXiv: https://arxiv.org/abs/2406.12620 · HTML: https://mlsanity.com/paper/2406.12620/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 25 of its 47 references that are in the map.
- First posted on arXiv 18 Jun 2024.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling](https://mlsanity.com/paper/2304.01373/) (2023)
- [FitNets: Hints for Thin Deep Nets](https://mlsanity.com/paper/1412.6550/) (2014)
- [Understanding intermediate layers using linear classifier probes](https://mlsanity.com/paper/1610.01644/) (2016)
- [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://mlsanity.com/paper/2405.21060/) (2024)
- [Probing Classifiers: Promises, Shortcomings, and Advances](https://mlsanity.com/paper/2102.12452/) (2021)
- [Designing and Interpreting Probes with Control Tasks](https://mlsanity.com/paper/1909.03368/) (2019)
- [What do you learn from context? Probing for sentence structure in contextualized word representations](https://mlsanity.com/paper/1905.06316/) (2019)
- [The Platonic Representation Hypothesis](https://mlsanity.com/paper/2405.07987/) (2024)
- [Better & Faster Large Language Models via Multi-token Prediction](https://mlsanity.com/paper/2404.19737/) (2024)
- [Insights on representational similarity in neural networks with canonical correlation](https://mlsanity.com/paper/1806.05759/) (2018)
- [Analysis Methods in Neural Language Processing: A Survey](https://mlsanity.com/paper/1812.08951/) (2018)
- [Using the Output Embedding to Improve Language Models](https://mlsanity.com/paper/1608.05859/) (2016)
- [Fast Differentiable Sorting and Ranking](https://mlsanity.com/paper/2002.08871/) (2020)
- [Generalized Shape Metrics on Neural Representations](https://mlsanity.com/paper/2110.14739/) (2021)
- [Similarity Analysis of Contextual Word Representation Models](https://mlsanity.com/paper/2005.01172/) (2020)
- [A Weighted Correlation Index for Rankings with Ties](https://mlsanity.com/paper/1404.3325/) (2014)
- [Probing Classifiers are Unreliable for Concept Removal and Detection](https://mlsanity.com/paper/2207.04153/) (2022)
- [Blackbox meets blackbox: Representational Similarity and Stability Analysis of Neural Language Models and Brains](https://mlsanity.com/paper/1906.01539/) (2019)
- [Beyond Geometry: Comparing the Temporal Structure of Computation in Neural Circuits with Dynamical Similarity Analysis](https://mlsanity.com/paper/2306.10168/) (2023)
- [Interpreting Encoding and Decoding Models](https://mlsanity.com/paper/1812.00278/) (2018)
- [Probing for Constituency Structure in Neural Language Models](https://mlsanity.com/paper/2204.06201/) (2022)
- [Metric Learning Encoding Models: A Multivariate Framework for Interpreting Neural Representations](https://mlsanity.com/paper/2402.11608/) (2024)

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

- [A Neural Model for Word Repetition](https://mlsanity.com/paper/2506.13450/) (2025)

## Abstract (arXiv)

Do architectural and training differences influence the way models represent and process language? Traditional similarity metrics tell us whether two models share a similar representational geometry, but they cannot explain why. Here, we propose a new, simple, approach to address this question. This approach maps neural activity in each model layer onto a set of interpretable linguistic features and quantifies how much each of them drives similarities and differences between models. We use this approach to compare 43 language models across 10 families, including decoder Transformers, State-Space Models, and Recurrent Neural Networks. We find that model-level similarity is driven most strongly by release date, a proxy for general LLM development, and model family, suggesting that linguistic signatures are not primarily shaped by scale or architecture class. Overall, our approach provides a way to link theoretically-motivated symbolic descriptions to neural representations and can readily be extended to other domains such as speech and vision, and to other neural systems such as biological brains.