# How to Protect Yourself from 5G Radiation? Investigating LLM Responses to Implicit Misinformation

arXiv: https://arxiv.org/abs/2503.09598 · HTML: https://mlsanity.com/paper/2503.09598/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 45 references that are in the map.
- First posted on arXiv 12 Mar 2025.

## Builds on (most cited first, 30 of 31)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [Towards Understanding Sycophancy in Language Models](https://mlsanity.com/paper/2310.13548/) (2023)
- [WildChat: 1M ChatGPT Interaction Logs in the Wild](https://mlsanity.com/paper/2405.01470/) (2024)
- [Large Language Models Are Not Robust Multiple Choice Selectors](https://mlsanity.com/paper/2309.03882/) (2023)
- [LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset](https://mlsanity.com/paper/2309.11998/) (2023)
- [Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment](https://mlsanity.com/paper/2308.05374/) (2023)
- [FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media](https://mlsanity.com/paper/1809.01286/) (2018)
- [TrustLLM: Trustworthiness in Large Language Models](https://mlsanity.com/paper/2401.05561/) (2024)
- [FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios](https://mlsanity.com/paper/2307.13528/) (2023)
- [WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild](https://mlsanity.com/paper/2406.04770/) (2024)
- [Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization](https://mlsanity.com/paper/2406.01171/) (2024)
- [Can LLM-Generated Misinformation Be Detected?](https://mlsanity.com/paper/2309.13788/) (2023)
- [The Earth is Flat because...: Investigating LLMs' Belief towards Misinformation via Persuasive Conversation](https://mlsanity.com/paper/2312.09085/) (2023)
- [CREPE: Open-Domain Question Answering with False Presuppositions](https://mlsanity.com/paper/2211.17257/) (2022)
- [(QA)2: Question Answering with Questionable Assumptions](https://mlsanity.com/paper/2212.10003/) (2022)
- [Won't Get Fooled Again: Answering Questions with False Premises](https://mlsanity.com/paper/2307.02394/) (2023)
- [Consent in Crisis: The Rapid Decline of the AI Data Commons](https://mlsanity.com/paper/2407.14933/) (2024)
- [Disinformation Detection: An Evolving Challenge in the Age of LLMs](https://mlsanity.com/paper/2309.15847/) (2023)
- [X-Eval: Generalizable Multi-aspect Text Evaluation via Augmented Instruction Tuning with Auxiliary Evaluation Aspects](https://mlsanity.com/paper/2311.08788/) (2023)
- [Holistic Evaluation for Interleaved Text-and-Image Generation](https://mlsanity.com/paper/2406.14643/) (2024)
- [Superficial Self-Improved Reasoners Benefit from Model Merging](https://mlsanity.com/paper/2503.02103/) (2025)
- [Human-in-the-loop Evaluation for Early Misinformation Detection: A Case Study of COVID-19 Treatments](https://mlsanity.com/paper/2212.09683/) (2022)
- [Web Retrieval Agents for Evidence-Based Misinformation Detection](https://mlsanity.com/paper/2409.00009/) (2024)
- [Correcting Negative Bias in Large Language Models through Negative Attention Score Alignment](https://mlsanity.com/paper/2408.00137/) (2024)
- [Stanceosaurus: Classifying Stance Towards Multilingual Misinformation](https://mlsanity.com/paper/2210.15954/) (2022)
- [Meta-Tuning LLMs to Leverage Lexical Knowledge for Generalizable Language Style Understanding](https://mlsanity.com/paper/2305.14592/) (2023)

## Built on by (most cited first, 18 of 18)

- [LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models](https://mlsanity.com/paper/2504.10430/) (2025)
- [When and What to Ask: AskBench and Rubric-Guided RLVR for LLM Clarification](https://mlsanity.com/paper/2602.11199/) (2026)
- [Don't Take the Premise for Granted: Evaluating the Premise Critique Ability of Large Language Models](https://mlsanity.com/paper/2505.23715/) (2025)
- [Tree-based Dialogue Reinforced Policy Optimization for Red-Teaming Attacks](https://mlsanity.com/paper/2510.02286/) (2025)
- [Unraveling Misinformation Propagation in LLM Reasoning](https://mlsanity.com/paper/2505.18555/) (2025)
- [RAG Makes Guardrails Unsafe? Investigating Robustness of Guardrails under RAG-style Contexts](https://mlsanity.com/paper/2510.05310/) (2025)
- [The Facade of Truth: Uncovering and Mitigating LLM Susceptibility to Deceptive Evidence](https://mlsanity.com/paper/2601.05478/) (2026)
- [The Synthetic Web: Adversarially-Curated Mini-Internets for Diagnosing Epistemic Weaknesses of Language Agents](https://mlsanity.com/paper/2603.00801/) (2026)
- [This Treatment Works, Right? Evaluating LLM Sensitivity to Patient Question Framing in Medical QA](https://mlsanity.com/paper/2604.05051/) (2026)
- [Senses Wide Shut: A Representation-Action Gap in Omnimodal LLMs](https://mlsanity.com/paper/2605.13737/) (2026)
- [Defending LLM-based Multi-Agent Systems Against Cooperative Attacks with Sentence-Level Rectification](https://mlsanity.com/paper/2605.28104/) (2026)
- [Investigating and Alleviating Harm Amplification in LLM Interactions](https://mlsanity.com/paper/2606.02423/) (2026)
- [Refining Critical Thinking in LLM Code Generation: A Faulty Premise-based Evaluation Framework](https://mlsanity.com/paper/2508.03622/) (2025)
- [ConspirED: A Dataset for Cognitive Traits of Conspiracy Theories and Large Language Model Safety](https://mlsanity.com/paper/2508.20468/) (2025)
- [Compositional Jailbreaking: An Empirical Analysis of Mutator Chain Interactions in Aligned LLMs](https://mlsanity.com/paper/2605.15598/) (2026)
- [MedRedFlag: Investigating how LLMs Redirect Misconceptions in Real-World Health Communication](https://mlsanity.com/paper/2601.09853/) (2026)
- [From Passive Response to Proactive Correction: Enhancing LLM Robustness Against Input Fact Perturbations](https://mlsanity.com/paper/2608.25894/) (2026)
- [RPCBench: A Benchmark for Proactive Premise Critique in LLM-based Recommendation](https://mlsanity.com/paper/2609.00918/) (2026)

## Abstract (arXiv)

As Large Language Models (LLMs) are widely deployed in diverse scenarios, the extent to which they could tacitly spread misinformation emerges as a critical safety concern. Current research primarily evaluates LLMs on explicit false statements, overlooking how misinformation often manifests subtly as unchallenged premises in real-world interactions. We curated EchoMist, the first comprehensive benchmark for implicit misinformation, where false assumptions are embedded in the query to LLMs. EchoMist targets circulated, harmful, and ever-evolving implicit misinformation from diverse sources, including realistic human-AI conversations and social media interactions. Through extensive empirical studies on 15 state-of-the-art LLMs, we find that current models perform alarmingly poorly on this task, often failing to detect false premises and generating counterfactual explanations. We also investigate two mitigation methods, i.e., Self-Alert and RAG, to enhance LLMs' capability to counter implicit misinformation. Our findings indicate that EchoMist remains a persistent challenge and underscore the critical need to safeguard against the risk of implicit misinformation.