Drivel-ology: Challenging LLMs with Interpreting Nonsense with Depth
yangwang825
Abstract
LLMs struggle with understanding the nuanced, context-dependent meanings of Drivelological text, which appears nonsensical but contains deeper semantic layers.
We introduce Drivelology, a unique linguistic phenomenon characterised as "nonsense with depth", utterances that are syntactically coherent yet pragmatically paradoxical, emotionally loaded, or rhetorically subversive. While such expressions may resemble surface-level nonsense, they encode implicit meaning requiring contextual inference, moral reasoning, or emotional interpretation. We find that current large language models (LLMs), despite excelling at many natural language processing (NLP) tasks, consistently fail to grasp the layered semantics of Drivelological text. To investigate this, we construct a small but diverse benchmark dataset of over 1,200 meticulously curated examples, with select instances in English, Mandarin, Spanish, French, Japanese, and Korean. Annotation was especially challenging: each of the examples required careful expert review to verify that it truly reflected Drivelological characteristics. The process involved multiple rounds of discussion and adjudication to address disagreements, highlighting the subtle and subjective nature of the Drivelology. We evaluate a range of LLMs on classification, generation, and reasoning tasks. Our results reveal clear limitations of LLMs: models often confuse Drivelology with shallow nonsense, produce incoherent justifications, or miss the implied rhetorical function altogether. These findings highlight a deeper representational gap in LLMs' pragmatic understanding and challenge the assumption that statistical fluency implies cognitive comprehension. We release our dataset and code to facilitate further research in modelling linguistic depth beyond surface-level coherence.
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Introducing Drivelology (幹話文學): a new linguistic phenomenon we define as "nonsense with depth." Our EMNLP 2025 (oral) paper presents a stress test with 1,200+ examples across 5 Drivelology types, revealing distinct failure modes across state-of-the-art LLMs.
Very impressive paper, there seems to be a large gap when trying to translate the Chinese language to English. Hope there are further studies in this area
@Harikyusocials Thanks for the thoughtful question! The gap you're noticing isn't just a Mandarin → English issue. Many Drivelology examples are deliberately "nonsense with depth": syntactically coherent but culturally loaded, paradoxical, or rhetorically subversive. That means some phrases depend heavily on prior cultural knowledge, social cues, or even irony embedded in everyday life.
When such examples are translated, the literal words can cross languages, but the Drivelological sense (the multi-layered humour, paradox, or social critique) often does not. For example, a pun, proverb inversion, or culturally embedded reference may only resonate with readers who share that cultural background. This isn't limited to Mandarin, similar issues arise in other languages as well.
So the difficulty is less about "translation quality" and more about how Drivelology encodes meaning at multiple levels, with implicit cultural or rhetorical signals that don't always carry over neatly. That's exactly why the paper emphasises Drivelology as a benchmark: it highlights the deep gap between surface fluency and genuine cultural-semantic understanding.
Hi, I understand the complexity of what this data conveys (it needs a reader not just fluent in the language but landscape and cultural background). What really hits me its the sources and some of the analysis, generally speaking I wouldn't agree with 40-45 percent of any of the Spanish results, and some of the puns are pretty particular about our use of exclamation and interrogation marks which are missing but the result gives an interesting interpretation (mainly because we drop nowadays ¿ and ¡ in touch screens but we are lazy, those text lacks good punctuation, and you can mess a phrase without those pretty badly -like genres are crazy required-). There is certain cultural interaction Mexican vs European -Castillian?- Spanish, but in like 10 minutes I found two context or paradox assignment that referred to Spain modern history that really really are too edgy (I probably could find the sources and are not originally Spanish but some French form of speaking about our pre democracy era), it's not a problem of taste but really cringed because I never heard a joke closer to it. Some others non spanish but with some spanish word-play texts have typos (mosa ->mosca) but the result is resolved with the closer best guess to the context and made the good assumptions, but again it's filling holes in bad initial data.
Why haven't you used any SOTA LLM in your study? Isn't it a major flaw, as the smallest LLMs are mostly useless in understanding the more complex language constructions anyways?
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