aiqbench analysis
This study introduces recall-based prompting strategies—Self-Recall and Question-Recall—that significantly improve LLM adherence to knowledge cutoffs, particularly for counterfactual questions where prior direct-answer methods fail. By requiring models to internally restate constraints and validate information against a specific temporal boundary, the approach outperforms conventional reasoning baselines across multiple benchmarks. The findings suggest that explicit internal verification mechanisms are critical for maintaining temporal consistency in large language models.
Can LLMs Be Constrained to the Past? Improving Knowledge Cutoff through Recall-Based Prompting
arXiv:2606.05804v1 Announce Type: new Abstract: Prompted knowledge cutoff instructs a large language model (LLM) to act as if information beyond a specified cutoff date were unavailable. However, prior work mainly relies on direct-answer generation, which struggles when post-cutoff knowledge is not explicitly queried but is only causally related to the question. To address this limitation, we propose two recall-based prompting strategies: Self-Recall (SR), which asks the model to restate its cutoff constraint, and Question-Recall (QR), which requires the model to recall question-relevant information valid under the cutoff. Across three existing benchmarks, our methods outperform both direct-answer prompting and conventional step-by-step reasoning baselines, with particularly strong improvements on counterfactual questions. To investigate robustness across different cutoff settings, we further construct the Multi-cutoff Historical Event Benchmark (MHEB), which evaluates the same question under multiple cutoff years. Results show that knowledge cutoff performance varies with cutoff distance, while combining SR and QR consistently yields the best performance.
Title:Can LLMs Be Constrained to the Past? Improving Knowledge Cutoff through Recall-Based Prompting
Abstract:Prompted knowledge cutoff instructs a large language model (LLM) to act as if information beyond a specified cutoff date were unavailable. However, prior work mainly relies on direct-answer generation, which struggles when post-cutoff knowledge is not explicitly queried but is only causally related to the question. To address this limitation, we propose two recall-based prompting strategies: Self-Recall (SR), which asks the model to restate its cutoff constraint, and Question-Recall (QR), which requires the model to recall question-relevant information valid under the cutoff. Across three existing benchmarks, our methods outperform both direct-answer prompting and conventional step-by-step reasoning baselines, with particularly strong improvements on counterfactual questions. To investigate robustness across different cutoff settings, we further construct the Multi-cutoff Historical Event Benchmark (MHEB), which evaluates the same question under multiple cutoff years. Results show that knowledge cutoff performance varies with cutoff distance, while combining SR and QR consistently yields the best performance.
Computation and Language (cs.CL)
arXiv:2606.05804 [cs.CL]
(or arXiv:2606.05804v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.05804 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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