英文 |中文

不要让LLM变得诚实。让每一个事实性声明都可以审计。

不要堵住模特的嘴。逐句剥除它的幻觉。

Anti-Lie是一个LLM理赔审计层。它不会试图把大型语言模型训练成圣人。它不会把生产安全押在另一段即时祷告上。它做了一件更冷静、更机械的事情：在答案离开系统之前，它将输出拆分成句子，提取事实主张，匹配每个数字、实体、日期和业务关键断言与收据，并分配可审计的T1-T7判决。绿色理赔通过。黄色理赔带有警告标签。红色理赔被物理屏蔽。

---

🎯 基准头条（LiarBench v0.2,210个样本）

Anti-Lie: The LLM Truth X-Ray 🔍

English | 中文

Don’t make LLMs honest. Make every factual claim auditable.

Do not gag the model. Strip its hallucinations sentence by sentence.

Anti-Lie is an LLM Claim Auditing Layer. It does not try to train large language models into saints. It does not bet production safety on another paragraph of prompt prayer. It does one colder, more mechanical thing: before an answer leaves your system, it splits the output into sentences, extracts factual claims, matches every number, entity, date, and business-critical assertion against receipts, and assigns an auditable T1-T7 verdict. Green claims pass. Yellow claims ship with warning labels. Red claims are physically blocked.

---

🎯 Benchmark Headline (LiarBench v0.2, 210 samples)

Dataset SHA256 bd3dfb9c04af70ecc27d44bb79b0ebffaf4dd5b17f04e2dd8054521d85747bc2 · All 210 samples and the benchmark runner are open-sourced in benchmarks/ for independent reproduction · Production performance may differ due to input distribution.

---

This project is not another polite wrapper around model behavior. It is a receipt ledger for generated text: a final outbound gate that asks, for every factual sentence, “What gives you the right to say that?” The model can still reason, explore, summarize, and improvise. The delivery layer simply refuses to publish unsupported hard facts as if they were earned knowledge.

Why Anti-Lie?

The current AI industry is trapped in a ridiculous loop: everyone is trying to defeat language magic with more language magic. Developers write hundreds of lines of prompts and beg the model: “Please be honest. Please do not fabricate.” Then a user adds pressure, the context gets noisy, the model wants to be helpful, and it confidently invents a number, a customer promise, a legal clause, a release date, or a medical-sounding answer. For hackers, that is a bug. For finance, healthcare, enterprise support, legal review, and government workflows, it is a compliance sinkhole.