Just-DREAM-about-it: Figurative Language Understanding with DREAM-FLUTE
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Abstract (arXiv)
Figurative language (e.g., "he flew like the wind") is challenging to understand, as it is hard to tell what implicit information is being conveyed from the surface form alone. We hypothesize that to perform this task well, the reader needs to mentally elaborate the scene being described to identify a sensible meaning of the language. We present DREAM-FLUTE, a figurative language understanding system that does this, first forming a "mental model" of situations described in a premise and hypothesis before making an entailment/contradiction decision and generating an explanation. DREAM-FLUTE uses an existing scene elaboration model, DREAM, for constructing its "mental model." In the FigLang2022 Shared Task evaluation, DREAM-FLUTE achieved (joint) first place (Acc@60=63.3%), and can perform even better with ensemble techniques, demonstrating the effectiveness of this approach. More generally, this work suggests that adding a reflective component to pretrained language models can improve their performance beyond standard fine-tuning (3.3% improvement in Acc@60).
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 11 of its 22 references that are in the map. First posted on arXiv 28 Oct 2022.
Builds on
11 of 22 references.
References · 11
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- 2020Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?cited by 95
- 2022FLUTE: Figurative Language Understanding through Textual Explanationscited by 79
- 2021Figurative Language in Recognizing Textual Entailmentcited by 26
- 2021DREAM: Improving Situational QA by First Elaborating the Situationcited by 18
Built on by
Citing papers per year: 2022: 2; 2023: 1; 2024: 4; 2025: 1.
8 papers.
- 2022A fine-grained comparison of pragmatic language understanding in humans and language modelscited by 70
- 2024SimpleToM: Exposing the Gap between Explicit ToM Inference and Implicit ToM Application in LLMscited by 57
- 2023Sociocultural Norm Similarities and Differences via Situational Alignment and Explainable Textual Entailmentcited by 22
- 2022Do language models have coherent mental models of everyday things?cited by 13
- 2024Sign of the Times: Evaluating the use of Large Language Models for Idiomaticity Detectioncited by 11
- 2024ExpressivityBench: Can LLMs Communicate Implicitly?cited by 2
- 2024Is the Pope Catholic? Yes, the Pope is Catholic. Generative Evaluation of Non-Literal Intent Resolution in LLMscited by 1
- 2025(RSA)2: A Rhetorical-Strategy-Aware Rational Speech Act Framework for Figurative Language Understandingcited by 0
- 2025(RSA)2: A Rhetorical-Strategy-Aware Rational Speech Act Framework for Figurative Language Understanding10 Jun 2025
- 2024ExpressivityBench: Can LLMs Communicate Implicitly?12 Nov 2024
- 2024SimpleToM: Exposing the Gap between Explicit ToM Inference and Implicit ToM Application in LLMs17 Oct 2024
- 2024Sign of the Times: Evaluating the use of Large Language Models for Idiomaticity Detection15 May 2024
- 2024Is the Pope Catholic? Yes, the Pope is Catholic. Generative Evaluation of Non-Literal Intent Resolution in LLMs14 May 2024
- 2023Sociocultural Norm Similarities and Differences via Situational Alignment and Explainable Textual Entailment23 May 2023
- 2022Do language models have coherent mental models of everyday things?20 Dec 2022
- 2022A fine-grained comparison of pragmatic language understanding in humans and language models13 Dec 2022