# LLM and Human Modes of Representation

arXiv: https://arxiv.org/abs/2606.21616 · HTML: https://mlsanity.com/paper/2606.21616/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 19 Jun 2026.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies](https://mlsanity.com/paper/1611.01368/) (2016)
- [Can language models handle recursively nested grammatical structures? A case study on comparing models and humans](https://mlsanity.com/paper/2210.15303/) (2022)
- [Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences](https://mlsanity.com/paper/2301.08571/) (2023)
- [How Furiously Can Colourless Green Ideas Sleep? Sentence Acceptability in Context](https://mlsanity.com/paper/2004.00881/) (2020)
- [Testing the Generalization Power of Neural Network Models Across NLI Benchmarks](https://mlsanity.com/paper/1810.09774/) (2018)
- [FF-NSL: Feed-Forward Neural-Symbolic Learner](https://mlsanity.com/paper/2106.13103/) (2021)
- [To model human linguistic prediction, make LLMs less superhuman](https://mlsanity.com/paper/2510.05141/) (2025)
- [MSciNLI: A Diverse Benchmark for Scientific Natural Language Inference](https://mlsanity.com/paper/2404.08066/) (2024)
- [A Knapsack by Any Other Name: Presentation impacts LLM performance on NP-hard problems](https://mlsanity.com/paper/2502.13776/) (2025)
- [NLI Data Sanity Check: Assessing the Effect of Data Corruption on Model Performance](https://mlsanity.com/paper/2104.04751/) (2021)
- [Predicting Sentence Acceptability Judgments in Multimodal Contexts](https://mlsanity.com/paper/2602.20918/) (2026)
- [Humans vs Vision-Language Models: A Unified Measure of Narrative Coherence](https://mlsanity.com/paper/2603.25537/) (2026)

## Abstract (arXiv)

Much work on the cognitive foundations of AI has focussed on comparisons between the ways in which Large Language Models (LLMs) and humans process information and represent it. One aspect of this comparison involves determining the extent to which LLMs can achieve or surpass human performance on a variety of cognitively interesting tasks. A second explores points of convergence and divergence between LLM and human systems for processing information. Here, I consider some recent research that has addressed both issues in two informational domains. The first is the representation of linguistic knowledge. The second is real world reasoning and planning. While LLMs frequently achieve impressive levels of performance and fluency on linguistic applications, they tend to handle linguistic content in ways that are distinct from human processing. They are also, for the most part, less efficient than humans in learning and generalisation for reasoning tasks.