# The Zero Body Problem: Probing LLM Use of Sensory Language

arXiv: https://arxiv.org/abs/2504.06393 · HTML: https://mlsanity.com/paper/2504.06393/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 44 references that are in the map.
- First posted on arXiv 8 Apr 2025.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [BERT Rediscovers the Classical NLP Pipeline](https://mlsanity.com/paper/1905.05950/) (2019)
- [The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets](https://mlsanity.com/paper/2310.06824/) (2023)
- [Language Models Represent Space and Time](https://mlsanity.com/paper/2310.02207/) (2023)
- [Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color](https://mlsanity.com/paper/2109.06129/) (2021)
- [GroundingGPT:Language Enhanced Multi-modal Grounding Model](https://mlsanity.com/paper/2401.06071/) (2024)
- [Large language models predict human sensory judgments across six modalities](https://mlsanity.com/paper/2302.01308/) (2023)
- [Visual Commonsense in Pretrained Unimodal and Multimodal Models](https://mlsanity.com/paper/2205.01850/) (2022)
- [The World of an Octopus: How Reporting Bias Influences a Language Model's Perception of Color](https://mlsanity.com/paper/2110.08182/) (2021)
- [Symbol Emergence in Robotics: A Survey](https://mlsanity.com/paper/1509.08973/) (2015)
- [Gromov-Wasserstein unsupervised alignment reveals structural correspondences between the color similarity structures of humans and large language models](https://mlsanity.com/paper/2308.04381/) (2023)
- [What Do Language Models Hear? Probing for Auditory Representations in Language Models](https://mlsanity.com/paper/2402.16998/) (2024)
- [Sniff AI: Is My 'Spicy' Your 'Spicy'? Exploring LLM's Perceptual Alignment with Human Smell Experiences](https://mlsanity.com/paper/2411.06950/) (2024)
- [Language Models Don't Learn the Physical Manifestation of Language](https://mlsanity.com/paper/2402.11349/) (2024)
- [Exploring Multimodal Perception in Large Language Models Through Perceptual Strength Ratings](https://mlsanity.com/paper/2503.06980/) (2025)
- [TouchAI: Exploring human-AI perceptual alignment in touch through language model representations](https://mlsanity.com/paper/2406.06587/) (2024)
- [The language of sounds unheard: Exploring musical timbre semantics of large language models](https://mlsanity.com/paper/2304.07830/) (2023)

## Built on by (most cited first, 5 of 5)

- [NarraBench: A Comprehensive Framework for Narrative Benchmarking](https://mlsanity.com/paper/2510.09869/) (2025)
- [The Limits of Learning from Pictures and Text: Vision-Language Models and Embodied Scene Understanding](https://mlsanity.com/paper/2603.26589/) (2026)
- [AI as a Tool for Simulation-Based Experiments in Literary Studies](https://mlsanity.com/paper/2606.02293/) (2026)
- [How LLMs Build Fictional Worlds: Setting and Narrative Space in AI-Generated Creative Storytelling](https://mlsanity.com/paper/2609.02482/) (2026)
- [Where Does the Sound Go? Tracing Acoustic Information Loss in Audio-Conditioned LLMs](https://mlsanity.com/paper/2609.05871/) (2026)

## Abstract (arXiv)

Sensory language expresses embodied experiences ranging from taste and sound to excitement and stomachache. This language is of interest to scholars from a wide range of domains including robotics, narratology, linguistics, and cognitive science. In this work, we explore whether language models, which are not embodied, can approximate human use of embodied language. We extend an existing corpus of parallel human and model responses to short story prompts with an additional 18,000 stories generated by 18 popular models. We find that all models generate stories that differ significantly from human usage of sensory language, but the direction of these differences varies considerably between model families. Namely, Gemini models use significantly more sensory language than humans along most axes whereas most models from the remaining five families use significantly less. Linear probes run on five models suggest that they are capable of identifying sensory language. However, we find preliminary evidence suggesting that instruction tuning may discourage usage of sensory language. Finally, to support further work, we release our expanded story dataset.