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New algorithm efficiently estimates language model context attribution

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new algorithm to efficiently estimate context attribution for language models. This method significantly reduces the number of model evaluations required, from n to approximately sqrt(log(1/epsilon))/epsilon, while maintaining a low error rate. The algorithm leverages sketching techniques applied to arithmetic circuits and backpropagation, improving upon existing methods that would require a much higher number of evaluations. This work also includes a theoretical lower bound to demonstrate the optimality of the proposed approach. AI

IMPACT This research offers a more efficient method for understanding how specific parts of a prompt influence language model outputs, potentially improving model interpretability and debugging.

RANK_REASON The item describes a new algorithm and theoretical bounds for context attribution in language models, presented in a research paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New algorithm efficiently estimates language model context attribution

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The item describes a new algorithm and theoretical bounds for context attribution in language models, presented in a research paper. [lever_c_demoted from research: ic=1 ai=1.0]
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  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Sketching Language Models for Context Attribution

    Context attribution asks how removing parts of a prompt changes a language model's output. We study leave-one-out (LOO) attribution for a prompt of $n$ tokens and a fixed measurement function. Computing all LOO values directly requires $n$ model evaluations. Under a stability ass…

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