  Elaborating a series of intermediate reasoning steps significantly improves
the ability of large language models (LLMs) to solve complex problems, as such
steps would evoke LLMs to think sequentially. However, human sarcasm
understanding is often considered an intuitive and holistic cognitive process,
in which various linguistic, contextual, and emotional cues are integrated to
form a comprehensive understanding of the speaker's true intention, which is
argued not be limited to a step-by-step reasoning process. To verify this
argument, we introduce a new prompting framework called SarcasmCue, which
contains four prompting strategies, $viz.$ chain of contradiction (CoC), graph
of cues (GoC), bagging of cues (BoC) and tensor of cues (ToC), which elicits
LLMs to detect human sarcasm by considering sequential and non-sequential
prompting methods. Through a comprehensive empirical comparison on four
benchmarking datasets, we show that the proposed four prompting methods
outperforms standard IO prompting, CoT and ToT with a considerable margin, and
non-sequential prompting generally outperforms sequential prompting.
