Cover Image

Fool Me, Fool Me: User Attitudes Toward LLM Falsehoods

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Nirman, Diana Bar-Or, Weizman, Ariel, Azaria, Amos
Format:	Preprint
Published:	2024
Subjects:	Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

The Self-Execution Benchmark: Measuring LLMs' Attempts to Overcome Their Lack of Self-Execution
by: Ezra, Elon, et al.
Published: (2025)

Fool Me Once? Contrasting Textual and Visual Explanations in a Clinical Decision-Support Setting
by: Kayser, Maxime, et al.
Published: (2024)

One Token to Fool LLM-as-a-Judge
by: Zhao, Yulai, et al.
Published: (2025)

TALL -- A Trainable Architecture for Enhancing LLM Performance in Low-Resource Languages
by: Ofer, Moshe, et al.
Published: (2025)

RAFT: Realistic Attacks to Fool Text Detectors
by: Wang, James, et al.
Published: (2024)

Don't be a Fool: Pooling Strategies in Offensive Language Detection from User-Intended Adversarial Attacks
by: Yu, Seunguk, et al.
Published: (2024)

Fooling the Textual Fooler via Randomizing Latent Representations
by: Hoang, Duy C., et al.
Published: (2023)

Language Model Re-rankers are Fooled by Lexical Similarities
by: Hagström, Lovisa, et al.
Published: (2025)

Fooling the LVLM Judges: Visual Biases in LVLM-Based Evaluation
by: Hwang, Yerin, et al.
Published: (2025)

Too Big to Fool: Resisting Deception in Language Models
by: Samsami, Mohammad Reza, et al.
Published: (2024)

PHANTOM RECALL: When Familiar Puzzles Fool Smart Models
by: Mukhopadhyay, Souradeep, et al.
Published: (2025)

Deceptive Automated Interpretability: Language Models Coordinating to Fool Oversight Systems
by: Lermen, Simon, et al.
Published: (2025)

Fooling LLM graders into giving better grades through neural activity guided adversarial prompting
by: Yamamura, Atsushi, et al.
Published: (2024)

Friend or Foe: How LLMs' Safety Mind Gets Fooled by Intent Shift Attack
by: Ding, Peng, et al.
Published: (2025)

How Easy is It to Fool Your Multimodal LLMs? An Empirical Analysis on Deceptive Prompts
by: Qian, Yusu, et al.
Published: (2024)

The State Of TTS: A Case Study with Human Fooling Rates
by: Varadhan, Praveen Srinivasa, et al.
Published: (2025)

Dagger Behind Smile: Fool LLMs with a Happy Ending Story
by: Song, Xurui, et al.
Published: (2025)

Ruffle&Riley: Insights from Designing and Evaluating a Large Language Model-Based Conversational Tutoring System
by: Schmucker, Robin, et al.
Published: (2024)

Know Me, Respond to Me: Benchmarking LLMs for Dynamic User Profiling and Personalized Responses at Scale
by: Jiang, Bowen, et al.
Published: (2025)

StyleFool: Fooling Video Classification Systems via Style Transfer
by: Cao, Yuxin, et al.
Published: (2022)

FeatureFool: Zero-Query Fooling of Video Models via Feature Map
by: Tang, Duoxun, et al.
Published: (2025)

A Wolf in Sheep's Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily
by: Ding, Peng, et al.
Published: (2023)

A Mousetrap: Fooling Large Reasoning Models for Jailbreak with Chain of Iterative Chaos
by: Yao, Yang, et al.
Published: (2025)

Don't Fool Me Twice: Adapting to Adversity in the Wild with Experience-Driven Reasoning
by: Ravie, Navin Sriram, et al.
Published: (2026)

Fool Me Once: A Case of Recurrent Delirium in the Setting of Buprenorphine Use
by: Yasmeen Abdo, et al.
Published: (2025)

Stress-testing Machine Generated Text Detection: Shifting Language Models Writing Style to Fool Detectors
by: Pedrotti, Andrea, et al.
Published: (2025)

Language Models Optimized to Fool Detectors Still Have a Distinct Style (And How to Change It)
by: Soto, Rafael Rivera, et al.
Published: (2025)

Unmasking Digital Falsehoods: A Comparative Analysis of LLM-Based Misinformation Detection Strategies
by: Huang, Tianyi, et al.
Published: (2025)

Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents
by: Qian, Cheng, et al.
Published: (2024)

SequentialBreak: Large Language Models Can be Fooled by Embedding Jailbreak Prompts into Sequential Prompt Chains
by: Saiem, Bijoy Ahmed, et al.
Published: (2024)

The Psychology of Falsehood: A Human-Centric Survey of Misinformation Detection
by: Nandi, Arghodeep, et al.
Published: (2025)

"This Wasn't Made for Me": Recentering User Experience and Emotional Impact in the Evaluation of ASR Bias
by: Liang, Siyu, et al.
Published: (2026)

Poor Fools
by: Daily, Jay E.
Published: (1972)

Use Me Wisely: AI-Driven Assessment for LLM Prompting Skills Development
by: Ognibene, Dimitri, et al.
Published: (2025)

The Energy of Falsehood: Detecting Hallucinations via Diffusion Model Likelihoods
by: Gautam, Arpit Singh, et al.
Published: (2026)

Me-Agent: A Personalized Mobile Agent with Two-Level User Habit Learning for Enhanced Interaction
by: Wang, Shuoxin, et al.
Published: (2026)

Hear Me, See Me, Understand Me: Audio-Visual Autism Behavior Recognition
by: Deng, Shijian, et al.
Published: (2024)

Teach Me Sign: Stepwise Prompting LLM for Sign Language Production
by: An, Zhaoyi, et al.
Published: (2025)

Just as Humans Need Vaccines, So Do Models: Model Immunization to Combat Falsehoods
by: Raza, Shaina, et al.
Published: (2025)

QUB-Cirdan at "Discharge Me!": Zero shot discharge letter generation by open-source LLM
by: Guo, Rui, et al.
Published: (2024)