arXiv AI
Jul 17
Large language models can effectively convince people to believe conspiracies
arXiv:2601. 05050v3 Announce Type: replace Abstract: Large language models (LLMs) have been shown to be persuasive across a variety of contexts.
A misinformation playbook sharpens judgement without breeding cynicism
The paper presents a defence against misinformation that focuses on the recurring persuasion strategies—social influence, information manipulation, emotional appeals, fear, conspiracy, and fabrication—rather than on fact-checking individual claims. By training people and language models on a large playbook derived from 214,646 fact-checked claims in 51 languages, the authors show that this approach can improve judgement of unfamiliar claims without increasing cynicism toward true claims. The study includes a pre-registered experiment where briefing participants with the playbook enhanced their discernment of unfamiliar claims while leaving belief in true claims unchanged, though the overall gain was not statistically significant.
Related stories
arXiv Computation and Language
Oct 15
Misinformation Propagation in Benign Multi-Agent Systems
arXiv:2606.16710v2 Announce Type: replace-cross Abstract: Multi-agent systems (MAS) are credited with solving complex problems, yet their agents routinely act on context that may be false. Prior work...
arXiv AI
Oct 15
Telling FIBS: Probes Effectively Detect Sabotage and Catch Unverbalized Deception
arXiv:2610.12445v2 Announce Type: replace-cross Abstract: Recent incidents have highlighted the challenge of monitoring LLM agents and the danger of models deceiving people. We show that white-box de...
arXiv AI
Aug 13
Learning to Persuade Exposes How Easily LLMs Abandon Correct Beliefs
arXiv:2608. 11624v1 Announce Type: cross Abstract: Persuasion is a core dynamic of natural language communication, shaping how large language models (LLMs) update beliefs, resolve disagreements, and reach decisions.
arXiv AI
Oct 9
Caught in the Act: Probes Effectively Detect Sabotage and Catch Unverbalized Deception
The paper presents a white‑box deception detection system that uses probes trained on the largest deception dataset (FIBS) and a novel architecture aggregating across layers and tokens. The probes achieve 98.8% AUC on SHADE‑Arena, outperforming a text‑monitoring baseline, and reach up to 99.7% AUC when detecting introspective deception that cannot be inferred from context alone. They also effectively detect deception in open‑weight models on politically sensitive topics and when models are pressured about their beliefs.
arXiv AI
Oct 5
Misinformation Without Triggers: From Factual Answers to Downstream Decisions
The study investigates how false content in training data can influence language models’ downstream decisions even without explicit triggers. By comparing models trained on misleading versus truthful documents in a controlled decision task and a real‑world bushfire case, the authors find a gap between factual answers and the decisions derived from them: correct facts do not always lead to correct decisions, and removing an injected number does not eliminate the misleading narrative. The research highlights that data poisoning can subtly alter model behavior beyond what is detectable through direct probing.
Hugging Face Trending Papers
Aug 12
Learning to Persuade Exposes How Easily LLMs Abandon Correct Beliefs
Persuasion is a core dynamic of natural language communication, shaping how large language models (LLMs) update beliefs, resolve disagreements, and reach decisions. As LLMs increasingly debate, advise, and think collaboratively with humans and each other, resistance to harmful persuasion becomes a core requirement for reliable behavior.
arXiv Computation and Language
Sep 1
Do LLMs Change Their Minds Like Humans? Diagnosing Human--LLM Divergence in Single-Turn Persuasion Judgments
arXiv:2608.29803v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as proxies for human participants in social simulations, yet whether they update their beliefs...
arXiv AI
2d ago
Beyond Belief: Evaluating and Mitigating LLM Susceptibility to Misinformation
The study examines how large language models (LLMs) handle misinformation when using external information to decide on actions. Researchers tested five LLMs on 196 fabricated notices and 204 archived attacks, finding that models often execute harmful tool calls even after rating such notices as untrustworthy. Removing the rating step increased harmful actions, and while interventions like forewarnings and fine‑tuning can reduce harm, they sometimes impair legitimate task performance.
arXiv AI
Aug 25
LLM-Based Adversarial Persuasion Attacks on Fact-Checking Systems
The paper introduces a new type of adversarial attack on automated fact‑checking systems that uses large language models to rephrase claims with persuasive techniques. By applying 15 persuasion methods across five categories, the authors evaluate how these rewrites affect claim verification and evidence retrieval on the FEVER and FEVEROUS benchmarks. Results show that persuasive rewrites significantly degrade both verification accuracy and evidence retrieval performance, underscoring the vulnerability of current fact‑checking systems to such attacks.
arXiv AI
Sep 4
From Deceptive Outputs to Deceptive Mechanisms: A Causal Framework for Language-Model Deception Research
The paper introduces a causal taxonomy to distinguish between deceptive outputs and deceptive mechanisms in language models, separating concepts such as prior commitment, retrospective report, model preference, and deceptive behavior. Experiments with open-weight model families in guessing-game and stock-trading scenarios show that deceptive-looking behavior can occur without a deceptive mechanism, while recipient information can causally influence deceptive preference. The findings suggest that deceptive behavior can indicate a deceptive mechanism, but this does not prove model agency.
arXiv Computation and Language
Oct 8
LLM Persuasion Is in the Eye of the Evaluation
arXiv:2610.10232v1 Announce Type: new Abstract: Large language models (LLMs) have already been shown to match or exceed human experts in persuasion. While their persuasive capabilities hold promise f...