# LMs stand their Ground: Investigating the Effect of Embodiment in Figurative Language Interpretation by Language Models

arXiv: https://arxiv.org/abs/2305.03445 · HTML: https://mlsanity.com/paper/2305.03445/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 44 references that are in the map.
- First posted on arXiv 5 May 2023.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [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)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents](https://mlsanity.com/paper/2201.07207/) (2022)
- [ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks](https://mlsanity.com/paper/1912.01734/) (2019)
- [Experience Grounds Language](https://mlsanity.com/paper/2004.10151/) (2020)
- [On the Paradox of Learning to Reason from Data](https://mlsanity.com/paper/2205.11502/) (2022)
- [Testing the Ability of Language Models to Interpret Figurative Language](https://mlsanity.com/paper/2204.12632/) (2022)
- [Embodied BERT: A Transformer Model for Embodied, Language-guided Visual Task Completion](https://mlsanity.com/paper/2108.04927/) (2021)
- [Language (Re)modelling: Towards Embodied Language Understanding](https://mlsanity.com/paper/2005.00311/) (2020)

## Built on by (most cited first, 3 of 3)

- [Toward Informal Language Processing: Knowledge of Slang in Large Language Models](https://mlsanity.com/paper/2404.02323/) (2024)
- [Exploring Spatial Schema Intuitions in Large Language and Vision Models](https://mlsanity.com/paper/2402.00956/) (2024)
- [Probing Language Models' Gesture Understanding for Enhanced Human-AI Interaction](https://mlsanity.com/paper/2401.17858/) (2024)

## Abstract (arXiv)

Figurative language is a challenge for language models since its interpretation is based on the use of words in a way that deviates from their conventional order and meaning. Yet, humans can easily understand and interpret metaphors, similes or idioms as they can be derived from embodied metaphors. Language is a proxy for embodiment and if a metaphor is conventional and lexicalised, it becomes easier for a system without a body to make sense of embodied concepts. Yet, the intricate relation between embodiment and features such as concreteness or age of acquisition has not been studied in the context of figurative language interpretation concerning language models. Hence, the presented study shows how larger language models perform better at interpreting metaphoric sentences when the action of the metaphorical sentence is more embodied. The analysis rules out multicollinearity with other features (e.g. word length or concreteness) and provides initial evidence that larger language models conceptualise embodied concepts to a degree that facilitates figurative language understanding.