arXiv:2606.24083v1 Announce Type: cross Abstract: "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 it
The increasing cost of large language model inference due to computational demands makes efficiency a critical area of research at this moment.
This research provides a framework to understand and optimize the cost-performance trade-offs in LLMs, directly impacting their deployment and economic viability.
We now have a standardized methodology, Cavewoman, to evaluate how an LLM's performance and cost are affected by input and output compression, moving beyond anecdotal compression strategies.
- · AI developers
- · Cloud providers
- · Companies deploying LLMs
- · Inefficient LLM architectures
Wider adoption and lower operational costs for large language models.
New optimization techniques specific to input and output channels will emerge, further improving efficiency.
More sophisticated, multi-modal compression methods could be developed, impacting a broader range of AI applications.
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Read at arXiv cs.AI