SemEval-2025 Task 1: AdMIRe -- Advancing Multimodal Idiomaticity Representation
This addresses the problem of idiomaticity as an obstacle to robust semantic representation in NLP, particularly for researchers and practitioners working with multimodal and multilingual models, though it is incremental as it builds on existing LLMs and vision-language models.
The paper tackled the challenge of interpreting idiomatic expressions in NLP by introducing datasets and tasks for SemEval-2025 Task 1: AdMIRe, focusing on multimodal contexts and multiple languages, where the most effective methods achieved human-level performance.
Idiomatic expressions present a unique challenge in NLP, as their meanings are often not directly inferable from their constituent words. Despite recent advancements in Large Language Models (LLMs), idiomaticity remains a significant obstacle to robust semantic representation. We present datasets and tasks for SemEval-2025 Task 1: AdMiRe (Advancing Multimodal Idiomaticity Representation), which challenges the community to assess and improve models' ability to interpret idiomatic expressions in multimodal contexts and in multiple languages. Participants competed in two subtasks: ranking images based on their alignment with idiomatic or literal meanings, and predicting the next image in a sequence. The most effective methods achieved human-level performance by leveraging pretrained LLMs and vision-language models in mixture-of-experts settings, with multiple queries used to smooth over the weaknesses in these models' representations of idiomaticity.