# Contextualising (Im)plausible Events Triggers Figurative Language

arXiv: https://arxiv.org/abs/2604.07885 · HTML: https://mlsanity.com/paper/2604.07885/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 9 Apr 2026.

## Builds on (most cited first, 6 of 6)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Modeling Semantic Plausibility by Injecting World Knowledge](https://mlsanity.com/paper/1804.00619/) (2018)
- [Log Probabilities Are a Reliable Estimate of Semantic Plausibility in Base and Instruction-Tuned Language Models](https://mlsanity.com/paper/2403.14859/) (2024)
- [Modeling Event Plausibility with Consistent Conceptual Abstraction](https://mlsanity.com/paper/2104.10247/) (2021)
- [Can a Gorilla Ride a Camel? Learning Semantic Plausibility from Text](https://mlsanity.com/paper/1911.05689/) (2019)
- [A Dataset for Physical and Abstract Plausibility and Sources of Human Disagreement](https://mlsanity.com/paper/2404.04035/) (2024)

## Abstract (arXiv)

This work explores the connection between (non-)literalness and plausibility at the example of subject-verb-object events in English. We design a systematic setup of plausible and implausible event triples in combination with abstract and concrete constituent categories. Our analysis of human and LLM-generated judgments and example contexts reveals substantial differences between assessments of plausibility. While humans excel at nuanced detection and contextualization of (non-)literal vs. implausible events, LLM results reveal only shallow contextualization patterns with a bias to trade implausibility for non-literal, plausible interpretations.