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LLMs struggle with date math, new benchmark reveals

By PulseAugur Editorial · [1 sources] ·

A new dataset and testing harness called date-math-bench reveals that large language models struggle with basic date arithmetic. Across 101 randomized questions per model, common errors included miscalculating elapsed days versus ordinal day counts and incorrectly predicting the next day of the week. Specifically, business day calculations proved to be a significant blind spot for models like Claude Haiku, GPT-4o-mini, and Llama 3.3 70B, while Claude Sonnet and Qwen3-27B performed perfectly in this category. AI

IMPACT Highlights a critical gap in LLM reasoning capabilities, potentially impacting applications requiring temporal understanding.

RANK_REASON New benchmark and dataset released for evaluating LLM performance on date arithmetic. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

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

LLMs struggle with date math, new benchmark reveals

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New benchmark and dataset released for evaluating LLM performance on date arithmetic. [lever_c_demoted from research: ic=1 ai=1.0]
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  1. dev.to — LLM tag TIER_1 English(EN) · Maverick Y ·

    # LLMs Can't Reliably Do Date Math — And Now There's Data

    <p>Date arithmetic looks like the safest possible thing to hand an LLM. No ambiguity, no judgment call, just counting. That's exactly what makes it dangerous: it reads as confident and final, so it doesn't get double-checked the way a hedged or uncertain answer would.</p> <p>This…

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