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arXiv Computation and Language By Roy Xie, Junlin Wang, Paul Rosu, Chunyuan Deng, Bolun Sun, Zihao Lin, Bhuwan Dhingra

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

Read the original on arXiv Computation and Language →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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