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English(EN) A Grammar Stopped My Model Inventing FAR Clauses. It Also Made It Refuse the Real Ones

LLM语法可防止编造的FAR条款,但会增加拒绝率

一位开发者探索了使用带语法的约束解码来阻止LLM编造《联邦采购条例》(FAR)的条款编号。最初的尝试显示了很高的替换率,但在修改后,发现在面对需要特定条款编号的提示时,模型主要选择弃权或拒绝回答。虽然语法成功消除了编造的编号,但并未提高模型识别正确条款或区分真实与不存在条款的能力,导致高拒绝率而非准确检索。 AI

影响 约束解码可以保证不会出现编造的标识符,但不能保证准确性;检索增强可能仍然是必要的。

排序理由 开发者对LLM进行约束解码实验,以防止输出编造内容。

在 dev.to — LLM tag 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

LLM语法可防止编造的FAR条款,但会增加拒绝率

本文如何被排名

Signal score
0 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
开发者对LLM进行约束解码实验,以防止输出编造内容。
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
product, model release
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
9 days old
Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

报道来源 [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Raihan ·

    一个语法规则阻止了我的模型生成FAR条款,但也让它拒绝了真实的条款

    <p><em>Constrained decoding cut fabricated clause numbers from 57 of 60 to 0. With an abstain option added, a 1.5B model answered "none" to every question that named a clause number, real or not.</em></p> <p><a class="article-body-image-wrapper" href="https://media2.dev.to/dynami…