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New framework HOMER uses multi-role LLMs for humor caption generation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel framework called HOMER for generating humorous captions for images, addressing limitations in creativity and interpretability of existing large language model (LLM) approaches. This framework utilizes a humor theory called GTVH and involves a multi-role LLM collaboration. The system includes a conflicting-script extractor, a retrieval-augmented hierarchical imaginator, and a caption generator, which together aim to produce funny and diverse captions by focusing on script oppositions and creative expansion. AI

IMPACT This research introduces a novel approach to multi-modal humor generation, potentially improving LLM creativity and interpretability in creative tasks.

RANK_REASON The cluster contains an academic paper detailing a new framework for humor caption generation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework HOMER uses multi-role LLMs for humor caption generation

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The cluster contains an academic paper detailing a new framework for humor caption generation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Wenbo Shang, Yuxi Sun, Jing Ma, Xin Huang ·

    On the Wings of Imagination: Conflicting Script-based Multi-role Framework for Humor Caption Generation

    arXiv:2602.06423v2 Announce Type: replace Abstract: Humor is a commonly used and intricate human language in daily life. Humor generation, especially in multi-modal scenarios, is a challenging task for large language models (LLMs), which is typically as funny caption generation f…

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