{"slug": "cavewoman-how-large-language-models-behave-under-linguistic-input-and-output", "title": "CAVEWOMAN: How Large Language Models Behave Under Linguistic Input and Output Compression", "summary": "Researchers introduced Cavewoman, a two-channel evaluation protocol for large language models under linguistic compression, finding that output compression reduces costs by 1.4-3x on API and open-weight models, while input compression increases costs by ~1.15x and degrades accuracy. The study tested eight models on five datasets, revealing that input compression causes models to generate longer responses and diverge from unconstrained references.", "body_md": "arXiv:2606.24083v1 Announce Type: new\nAbstract: \"Talk short. Drop grammar. Save token.\" This caveman style is widely promoted as a way to cut inference cost, but whether it actually saves anything depends on which channel (the user's prompt or the model's response) is being compressed. We present Cavewoman, a two-channel evaluation protocol that scores every generation on task accuracy, realized per-item cost, and reference-text agreement against the model's unconstrained reference. We evaluate eight models on five datasets at five reduction levels, with both channels measured on the same items. Output compression cuts realized cost on most API models (1.4-2.4x per model, up to 3x in the best case) and on all four open-weight models under public-tier pricing. Input compression has the opposite effect, a strict lose-lose: it raises net cost rather than lowering it (~1.15x on the five-benchmark mean, up to 1.8x on the worst dataset and 2.7x under stronger compression), because models compensate with longer responses even as accuracy collapses. Under the same setting, surface text diverges from the unconstrained reference: on the non-reasoning models, roughly half of all generations are correct yet their surface text no longer entails the model's own unconstrained baseline generation. The divergence survives length-controlled re-scoring, multiple-comparisons correction, and replication under complementary semantic measures. Code and data are available at https://github.com/danielle34/cavewoman.", "url": "https://wpnews.pro/news/cavewoman-how-large-language-models-behave-under-linguistic-input-and-output", "canonical_source": "https://arxiv.org/abs/2606.24083", "published_at": "2026-06-24 04:00:00+00:00", "updated_at": "2026-06-24 04:16:17.915026+00:00", "lang": "en", "topics": ["large-language-models", "ai-infrastructure", "ai-research"], "entities": ["Cavewoman", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/cavewoman-how-large-language-models-behave-under-linguistic-input-and-output", "markdown": "https://wpnews.pro/news/cavewoman-how-large-language-models-behave-under-linguistic-input-and-output.md", "text": "https://wpnews.pro/news/cavewoman-how-large-language-models-behave-under-linguistic-input-and-output.txt", "jsonld": "https://wpnews.pro/news/cavewoman-how-large-language-models-behave-under-linguistic-input-and-output.jsonld"}}