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arXiv Computation and Language

Knowing When to Stop: Efficient Context Processing via Latent Sufficiency Signals

Large language models (LLMs) often process entire input contexts even when only a small portion is needed to answer a query. The authors introduce dynamic context cutoff, a technique that lets LLMs self‑terminate processing once they have gathered enough task‑relevant information. By analyzing model internals, they find that certain attention heads encode latent sufficiency signals that can be detected with lightweight classifiers, enabling efficient context handling. Experiments on six QA datasets with LLaMA, Qwen, and Mistral models (1B–70B) show a 3.4 % accuracy gain and a 1.33× reduction in token usage, outperforming other context‑efficiency methods at similar token reductions. The study also notes that larger models can intrinsically assess sufficiency through prompting, while smaller models need explicit probing.

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