English(EN) Stanford and UC Berkeley researchers have released TokenGate, a drop-in module that teaches an existing LLM to prune unimportant tokens from long prompts. In te

TokenGate 模块通过修剪提示 token 提高 LLM 效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-25 13:51

斯坦福大学和加州大学伯克利分校的研究人员开发了 TokenGate，这是一个旨在提高大型语言模型 (LLM) 效率的新模块。TokenGate 的功能是教会现有的 LLM 识别并删除冗长提示中不太重要的 token。初步测试表明，在不影响准确性的情况下，将首次 token 时间缩短了高达 11.3 倍，峰值内存使用量减少了高达 3.4 倍，取得了显著的改进。 AI

影响 TokenGate 减少处理时间和内存使用的能力，有可能显著降低 LLM 应用的运营成本和延迟。

排序理由详细介绍 LLM 效率新技术的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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Mastodon — mastodon.social TIER_1 English(EN) · [email protected] · 2026-10-25 13:51

Stanford and UC Berkeley researchers have released TokenGate, a drop-in module that teaches an existing LLM to prune unimportant tokens from long prompts. In te

Stanford and UC Berkeley researchers have released TokenGate, a drop-in module that teaches an existing LLM to prune unimportant tokens from long prompts. In tests up to 128k tokens it cut time-to-first-token by 1.4x to 11.3x and peak memory by up to 3.4x, with no accuracy loss. …

链接 marktechpost.com/…/stanford-berkeley-toke…

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Stanford and UC Berkeley researchers have released TokenGate, a drop-in module that teaches an existing LLM to prune unimportant tokens from long prompts. In te

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