Technical Debt in In-Context Learning: Diminishing Efficiency in Long Context

• URL: http://arxiv.org/abs/2502.04580v2
• Date: Sat, 25 Oct 2025 00:08:41 GMT
• Title: Technical Debt in In-Context Learning: Diminishing Efficiency in Long Context
• Authors: Taejong Joo, Diego Klabjan,
• Abstract summary: Transformers have demonstrated remarkable in-context learning capabilities, adapting to new tasks by simply conditioning on demonstrations without parameter updates.<n>It remains unclear to what extent the transformers optimally learn in-context compared to principled learning algorithms.
• Score: 24.905102026459428
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transformers have demonstrated remarkable in-context learning (ICL) capabilities, adapting to new tasks by simply conditioning on demonstrations without parameter updates. Compelling empirical and theoretical evidence suggests that ICL, as a general-purpose learner, could outperform task-specific models. However, it remains unclear to what extent the transformers optimally learn in-context compared to principled learning algorithms. To investigate this, we employ a meta ICL framework in which each prompt defines a distinctive regression task whose target function is drawn from a hierarchical distribution, requiring inference over both the latent model class and task-specific parameters. Within this setup, we benchmark sample complexity of ICL against principled learning algorithms, including the Bayes optimal estimator, under varying performance requirements. Our findings reveal a striking dichotomy: while ICL initially matches the efficiency of a Bayes optimal estimator, its efficiency significantly deteriorates in long context. Through an information-theoretic analysis, we show that the diminishing efficiency is inherent to ICL. These results clarify the trade-offs in adopting ICL as a universal problem solver, motivating a new generation of on-the-fly adaptive methods without the diminishing efficiency.

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