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A reflective essay using a blues song as a metaphor for how large language models generate text token by token, arguing that the 'throw' (generation) determines the 'aim' (intention), subverting the usual order of intention before expression.
The author recounts a two-day experiment using GPT-5.6 as a cognitive crutch and translator for self-understanding, discovering a productive loop of representation, resistance, correction, and reconstruction that may represent an early prototype of human-LLM symbiosis.
A metaphorical discussion about AI not enabling everyone to build their own app, stressing that one would only pursue something out of genuine passion, not just because the technology is feasible.
This paper investigates how large language models handle the combination of negation and figurative language, finding that this combination poses a particular challenge and that performance depends heavily on prompt style. The authors develop new annotations for the Fig-QA dataset and analyze embedding spaces to uncover additional linguistic factors like tense and concreteness.
This paper introduces MetaphorVU-Bench, the first systematic benchmark for metaphorical video understanding, and proposes MetaphorBoost, an inference-time enhancement framework that improves cross-domain mapping in multimodal large language models.
ViMU is the first benchmark designed to evaluate video understanding models' ability to interpret metaphorical, ironic, and social meanings beyond literal visual comprehension, using hint-free open-ended and multiple-choice questions.