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Found 6 papers, 5 papers with code
Don’t Go Far Off: An Empirical Study on Neural Poetry Translation
Moreover, multilingual fine-tuning on poetic data outperforms bilingual fine-tuning on poetic data.
I Spy a Metaphor: Large Language Models and Diffusion Models Co-Create Visual Metaphors
We propose to solve the task through the collaboration between Large Language Models (LLMs) and Diffusion Models: Instruct GPT-3 (davinci-002) with Chain-of-Thought prompting generates text that represents a visual elaboration of the linguistic metaphor containing the implicit meaning and relevant objects, which is then used as input to the diffusion-based text-to-image models. Using a human-AI collaboration framework, where humans interact both with the LLM and the top-performing diffusion model, we create a high-quality dataset containing 6, 476 visual metaphors for 1, 540 linguistic metaphors and their associated visual elaborations.
Sociocultural Norm Similarities and Differences via Situational Alignment and Explainable Textual Entailment
Embedding Chain-of-Thought prompting in a human-AI collaborative framework, we build a high-quality dataset of 3, 069 social norms aligned with social situations across Chinese and American cultures alongside corresponding free-text explanations.
FLUTE: Figurative Language Understanding through Textual Explanations
Figurative language understanding has been recently framed as a recognizing textual entailment (RTE) task (a. k. a.
Don't Go Far Off: An Empirical Study on Neural Poetry Translation
Moreover, multilingual fine-tuning on poetic data outperforms \emph{bilingual} fine-tuning on poetic data.
COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic
The dataset contains claims, evidence for the claims, and contradictory claims refuted by the evidence.
