English(EN) The 2B model scored 10%. 93% of its answers were right

小型本地LLM在日语分类任务中受限于token数而非理解能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-13 00:15

最近对小型本地运行的语言模型进行的评估显示，它们在日语分类任务上的表现受到token预算限制的严重阻碍，而非理解能力不足。LFM2.5-2.6B和Phi 4 Mini等模型常常在512个token的限制内无法生成完整答案，导致它们被判为错误，尽管它们的推理是准确的。当token预算增加时，这些模型的答案率和准确性有所提高，尽管有些模型仍然会达到上限或生成无法解析的输出。最终，即使有这些改进，在这些特定任务上，经过测试的本地模型并未超越托管API的准确性，API的优势在于其无法进行格式化失败。 AI

影响强调了token预算和输出格式在小型本地LLM实际应用中的关键作用。

排序理由该条目详细介绍了在特定任务上对多个小型语言模型的评估，比较了它们的性能并确定了局限性。[lever_c_demoted from research: ic=1 ai=1.0]

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Newsworthiness bucket: Tool; 该条目详细介绍了在特定任务上对多个小型语言模型的评估，比较了它们的性能并确定了局限性。[lever_c_demoted from research: ic=1 ai=1.0]
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dev.to — LLM tag TIER_1 English(EN) · Ikkun · 2026-11-13 00:15

2B模型得分10%。93%的答案是正确的

<p>I expected small local models to be worse than a hosted decision API at Japanese classification. I did not expect most of the gap to be something other than judgement. On the hardest of three tasks one model scored <strong>10.8% overall</strong> while getting <strong>27 of the…

本文如何被排名

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2B模型得分10%。93%的答案是正确的

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