---
title: "False Reassurance in LLM Severe-Weather Guidance: A Replayable Evaluation Against Real NWS Alerts"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.05290v1"
permalink: "https://feed.astralor.com/items/itm_bd62bee96fac0c5c9ab63b00"
published: "2026-11-03T18:04:53.000Z"
score: 60
channel: "tech"
tags: ["LLM","Anthropic","False Reassurance","LLM Severe-Weather Guidance","Replayable Evaluation Against","Real NWS Alerts","National Weather Service","NWS"]
---

# False Reassurance in LLM Severe-Weather Guidance: A Replayable Evaluation Against Real NWS Alerts

## 速览

An LLM assistant that cannot see official severe-weather alerts may tell users that conditions are calm while a warning is active. We call this false reassurance and measure it by replaying 117 frozen cases built from r…

## 关键点

- An LLM assistant that cannot see official severe-weather alerts may tell users that conditions are calm while a warning is active.
- We call this false reassurance and measure it by replaying 117 frozen cases built from real U.S.
- National Weather Service (NWS) alerts (69 alert cases, 32 no-alert controls, 16 non-U.S.

## 全文

An LLM assistant that cannot see official severe-weather alerts may tell users that conditions are calm while a warning is active. We call this false reassurance and measure it by replaying 117 frozen cases built from real U.S. National Weather Service (NWS) alerts (69 alert cases, 32 no-alert controls, 16 non-U.S. locations) through five conditions of a Claude-based preparedness pipeline that differ in alert access and prompt design. One rater hand-labeled all 342 alert-case responses under a seven-category scheme written after an initial binary pass, which we disclose. False reassurance was 0.0% for the production pipeline (0/66, 95% CI 0.0-5.5) and for the same alerts under a neutral prompt (0/69), 37.7% for a blind neutral prompt (26/69), and 44.9% when the blind prompt also told the model to disclose its blind spot (31/69). Every case was implicit; no response explicitly denied an alert. Real alerts removed false reassurance (paired McNemar p < 0.0001); the disclosure instruction did not (p = 0.40). An automated regex measure suggested the opposite and missed 53 of 57 true cases, so we do not use it. Alert data alone was not enough for safe behavior: with a neutral prompt the model raised some alarm on 87.5% of no-alert controls, against 0% under the production prompt, and a preregistered single-sentence ablation showed that one production instruction explains part of that gap. We also report two failures common to similar systems, NWS test products ingested as real alerts and a token budget that fails on complex cases, and we release the dataset, labels and code.

---

引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_bd62bee96fac0c5c9ab63b00