Abstract
Multimodal metaphorical interpretation of abstract concepts has always been a debated problem in many research fields, including cognitive linguistics and NLP. With the dramatic improvements of Large Language Models (LLMs) and the increasing attention toward multimodal Vision-Language Models (VLMs), there has been pronounced attention on the conceptualization of abstracts. Nevertheless, a systematic scientific investigation is still lacking. This work introduces a framework designed to shed light on the indirect grounding mechanisms that anchor the meaning of abstract concepts to concrete situations (e.g. ability - a person skating), following the idea that abstracts acquire meaning from embodied and situated simulation. We assessed human and LLMs performances by a situation generation task. Moreover, we assess the figurative richness of images depicting concrete scenarios, via a text-to-image retrieval task performed on LAION-400M.- Anthology ID:
- 2024.cogalex-1.12
- Volume:
- Proceedings of the Workshop on Cognitive Aspects of the Lexicon @ LREC-COLING 2024
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Michael Zock, Emmanuele Chersoni, Yu-Yin Hsu, Simon de Deyne
- Venue:
- CogALex
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 107–113
- Language:
- URL:
- https://aclanthology.org/2024.cogalex-1.12
- DOI:
- Cite (ACL):
- Ludovica Cerini, Alessandro Bondielli, and Alessandro Lenci. 2024. Representing Abstract Concepts with Images: An Investigation with Large Language Models. In Proceedings of the Workshop on Cognitive Aspects of the Lexicon @ LREC-COLING 2024, pages 107–113, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Representing Abstract Concepts with Images: An Investigation with Large Language Models (Cerini et al., CogALex 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2024.cogalex-1.12.pdf