LMs stand their Ground: Investigating the Effect of Embodiment in Figurative Language Interpretation by Language Models
This addresses the challenge of figurative language understanding in AI, providing insights into how embodiment influences model performance, though it is incremental in exploring existing features.
The study investigated how larger language models interpret figurative language, finding that they perform better with more embodied metaphors, ruling out confounding factors like word length or concreteness.
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.