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Sequential correlations impact in-context learning in sequence models

By PulseAugur Editorial · [2 sources] ·

A new research paper explores how sequential correlations in data affect in-context learning (ICL) within modern sequence models. The study, using a solvable model based on linear attention and tested on transformer architectures, identifies two key effects. First, correlations in prompts can effectively shorten the context length, making them behave like shorter independent example prompts. Second, when the query token is also correlated with the context, test errors decrease, especially for softmax attention compared to linear attention, suggesting that prompt correlations influence the optimal attention architecture for a given task. AI

IMPACT This research offers theoretical insights into how data correlations affect model performance, potentially guiding future architectural choices for improved in-context learning.

RANK_REASON The cluster contains an academic paper detailing new research findings on in-context learning.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Sequential correlations impact in-context learning in sequence models

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The cluster contains an academic paper detailing new research findings on in-context learning.
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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Mary Letey, Yue M. Lu, Cengiz Pehlevan, Jacob Zavatone-Veth ·

    Sequential Correlations Change In-Context Learning: Effective Context Length and Architectural Mismatch

    arXiv:2607.03660v1 Announce Type: new Abstract: Modern sequence models have a striking capacity for in-context learning (ICL); they can perform new tasks based only on examples given in the prompt. Understanding how this ability emerges requires theory that captures important pro…

  2. arXiv stat.ML TIER_1 English(EN) · Jacob Zavatone-Veth ·

    Sequential Correlations Change In-Context Learning: Effective Context Length and Architectural Mismatch

    Modern sequence models have a striking capacity for in-context learning (ICL); they can perform new tasks based only on examples given in the prompt. Understanding how this ability emerges requires theory that captures important properties of natural data. Linear regression has s…

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