aiqbench analysis
StrategyBench introduces a new evaluation framework designed to measure how well large language models can explicitly abstract task rules from few-shot examples, addressing limitations in standard in-context learning approaches. The research demonstrates that the utility of these explicit strategies varies significantly across different task categories and depends heavily on specific model configurations and adaptation settings. This benchmark provides essential insights for advancing robust task adaptation in data-scarce environments by quantifying the gap between implicit example usage and explicit rule induction.
StrategyBench: Evaluating Explicit Strategy Induction in Large Language Models
arXiv:2608.23475v1 Announce Type: new Abstract: As large language models are increasingly used in data-scarce and evolving task scenarios, few-shot in-context learning (ICL) has become a key paradigm for task adaptation. However, direct ICL often uses a small set of examples without explicitly abstracting task rules, making it sensitive to example construction. In contrast, human learners often reduce such sensitivity by first summarizing task rules from examples and then applying them to new instances. To evaluate this ability, we propose StrategyBench, which selects strategy-inducible tasks from BIG-Bench, constructs reference strategies, and defines evaluation metrics along two dimensions: strategy quality and downstream utility. We further analyze strategy induction from three perspectives: task variation, model configuration, and adaptation setting, covering category-wise differences, generator-executor choices, demonstration design, and SFT-based adaptation. Experiments show that explicit strategy utility differs substantially across task categories and depends on both strategy generation and execution conditions. The benchmark is released at: https://anonymous.4open.science/r/StrategyBench-D53C.
Abstract:As large language models are increasingly used in data-scarce and evolving task scenarios, few-shot in-context learning (ICL) has become a key paradigm for task adaptation. However, direct ICL often uses a small set of examples without explicitly abstracting task rules, making it sensitive to example construction. In contrast, human learners often reduce such sensitivity by first summarizing task rules from examples and then applying them to new instances. To evaluate this ability, we propose StrategyBench, which selects strategy-inducible tasks from BIG-Bench, constructs reference strategies, and defines evaluation metrics along two dimensions: strategy quality and downstream utility. We further analyze strategy induction from three perspectives: task variation, model configuration, and adaptation setting, covering category-wise differences, generator-executor choices, demonstration design, and SFT-based adaptation. Experiments show that explicit strategy utility differs substantially across task categories and depends on both strategy generation and execution conditions. The benchmark is released at: this https URL.
Artificial Intelligence (cs.AI)
arXiv:2608.23475 [cs.AI]
(or arXiv:2608.23475v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.23475 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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