Tailored untruths: How personalisation challenges LLM safeguards | Paper Citation Record

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This paper cites Bilaniuk, L., Melnyk, S.,.

Tailored untruths: How personalisation challenges LLM safeguards Bilaniuk, L., Melnyk, S.,

Reference 3

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This paper cites Specializing Large Language Models to Simulate Survey Response Distributions for Global Populations.

Tailored untruths: How personalisation challenges LLM safeguards Specializing Large Language Models to Simulate Survey Response Distributions for Global Populations

Reference 6

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Tailored untruths: How personalisation challenges LLM safeguards Can LLM-Generated Misinformation Be Detected?

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Tailored untruths: How personalisation challenges LLM safeguards From Persona to Personalization: A Survey on Role-Playing Language Agents

Reference 9

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Tailored untruths: How personalisation challenges LLM safeguards Multilingual Jailbreak Challenges in Large Language Models

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This paper cites Safeguarding Large Language Models: A Survey.

Tailored untruths: How personalisation challenges LLM safeguards Safeguarding Large Language Models: A Survey

Reference 12

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This paper cites Feng, S., Sorensen, T., Liu, Y., Fisher, J., Park, C.Y., Choi, Y., Tsvetkov, Y.,.

Tailored untruths: How personalisation challenges LLM safeguards Feng, S., Sorensen, T., Liu, Y., Fisher, J., Park, C.Y., Choi, Y., Tsvetkov, Y.,

Reference 14

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This paper cites MisinfoEval: Generative AI in the Era of "Alternative Facts".

Tailored untruths: How personalisation challenges LLM safeguards MisinfoEval: Generative AI in the Era of "Alternative Facts"

Reference 15

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This paper cites (Eds.), Proceedings of the 63rd AnnualMeetingoftheAssociationforComputationalLinguistics(Volume1:LongPapers),AssociationforComputationalLinguistics,Vienna, Austria.

Tailored untruths: How personalisation challenges LLM safeguards (Eds.), Proceedings of the 63rd AnnualMeetingoftheAssociationforComputationalLinguistics(Volume1:LongPapers),AssociationforComputationalLinguistics,Vienna, Austria

Reference 16

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This paper cites The Levers of Political Persuasion with Conversational AI.

Tailored untruths: How personalisation challenges LLM safeguards The Levers of Political Persuasion with Conversational AI

Reference 17

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This paper cites Proceedings of the International AAAI Conference on Web and Social Media 18, 542–556.

Tailored untruths: How personalisation challenges LLM safeguards Proceedings of the International AAAI Conference on Web and Social Media 18, 542–556

Reference 18

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Tailored untruths: How personalisation challenges LLM safeguards Lying Blindly: Bypassing ChatGPT's Safeguards to Generate Hard-to-Detect Disinformation Claims

Reference 19

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This paper cites FakeGPT: Fake News Generation, Explanation and Detection of Large Language Models.

Tailored untruths: How personalisation challenges LLM safeguards FakeGPT: Fake News Generation, Explanation and Detection of Large Language Models

Reference 21

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Tailored untruths: How personalisation challenges LLM safeguards Unresolved cited work

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This paper cites Proceedings of the AAAI Conference on Artificial Intelligence 36, 10803–10812.

Tailored untruths: How personalisation challenges LLM safeguards Proceedings of the AAAI Conference on Artificial Intelligence 36, 10803–10812

Reference 23

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This paper cites Catching Chameleons: Detecting Evolving Disinformation Generated using Large Language Models.

Tailored untruths: How personalisation challenges LLM safeguards Catching Chameleons: Detecting Evolving Disinformation Generated using Large Language Models

Reference 24

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This paper cites (Eds.), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Dublin, Ireland.

Tailored untruths: How personalisation challenges LLM safeguards (Eds.), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Dublin, Ireland

Reference 25

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This paper cites Nature Machine Intelligence 6, 383–392.

Tailored untruths: How personalisation challenges LLM safeguards Nature Machine Intelligence 6, 383–392

Reference 26

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This paper cites (Eds.), Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Singapore.

Tailored untruths: How personalisation challenges LLM safeguards (Eds.), Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Singapore

Reference 27

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This paper cites arXiv preprint arXiv:2502.11528.

Tailored untruths: How personalisation challenges LLM safeguards arXiv preprint arXiv:2502.11528

Reference 28

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This paper cites RECAP: Retrieval-Enhanced Context-Aware Prefix Encoder for Personalized Dialogue Response Generation.

Tailored untruths: How personalisation challenges LLM safeguards RECAP: Retrieval-Enhanced Context-Aware Prefix Encoder for Personalized Dialogue Response Generation

Reference 29

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Tailored untruths: How personalisation challenges LLM safeguards (Eds.), Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Singapore

Reference 31

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Tailored untruths: How personalisation challenges LLM safeguards HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal

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Tailored untruths: How personalisation challenges LLM safeguards DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature

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Tailored untruths: How personalisation challenges LLM safeguards Proceedings of the International AAAI Conference on Web and Social Media 17, 1052–1062

Reference 36

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Tailored untruths: How personalisation challenges LLM safeguards Measuring and Benchmarking Large Language Models' Capabilities to Generate Persuasive Language

Reference 38

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Tailored untruths: How personalisation challenges LLM safeguards Information Processing & Management 62, 104120

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Tailored untruths: How personalisation challenges LLM safeguards Persuasion with Large Language Models: A Survey of Empirical Evidence, Study Methodologies, and Ethical Implications

Reference 42

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Tailored untruths: How personalisation challenges LLM safeguards URL:https://arxiv.org/abs/2505.09662, arXiv:2505.09662

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Tailored untruths: How personalisation challenges LLM safeguards PNAS Nexus 3, pgae035

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Tailored untruths: How personalisation challenges LLM safeguards Beyond Release: Access Considerations for Generative AI Systems

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Tailored untruths: How personalisation challenges LLM safeguards A StrongREJECT for Empty Jailbreaks

Reference 47

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Tailored untruths: How personalisation challenges LLM safeguards Science Advances 9, eadh1850

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Tailored untruths: How personalisation challenges LLM safeguards Fake News Detectors are Biased against Texts Generated by Large Language Models

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Tailored untruths: How personalisation challenges LLM safeguards (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2024, Association for Computational Linguistics, Miami, Florida, USA

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Tailored untruths: How personalisation challenges LLM safeguards (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2021, Association for Computational Linguistics, Punta Cana, Dominican Republic

Reference 51

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Tailored untruths: How personalisation challenges LLM safeguards Report: Systemic issues

Reference 52

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Tailored untruths: How personalisation challenges LLM safeguards (Eds.), Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, Online

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Tailored untruths: How personalisation challenges LLM safeguards Automated Evaluation of Personalized Text Generation using Large Language Models

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Tailored untruths: How personalisation challenges LLM safeguards doi: https://doi.org/10.18653/v1/2023.eacl-demo

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Tailored untruths: How personalisation challenges LLM safeguards RedAgent: Red Teaming Large Language Models with Context-aware Autonomous Language Agent

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Tailored untruths: How personalisation challenges LLM safeguards BERTScore: Evaluating Text Generation with BERT

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Tailored untruths: How personalisation challenges LLM safeguards (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc

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Tailored untruths: How personalisation challenges LLM safeguards Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languages

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Tailored untruths: How personalisation challenges LLM safeguards The Curious Case of Neural Text Degeneration

Reference 2019

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Tailored untruths: How personalisation challenges LLM safeguards CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims

Reference 2020

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Tailored untruths: How personalisation challenges LLM safeguards doi:https://doi.org/10.51593/2021CA003

Reference 2021

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Tailored untruths: How personalisation challenges LLM safeguards Proceedings of the AAAI Conference on Artificial Intelligence 36, 10581–10589

Reference 2022

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Tailored untruths: How personalisation challenges LLM safeguards Explore, Establish, Exploit: Red Teaming Language Models from Scratch

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This paper cites URL: https://edmo.eu/wp-content/uploads/2023/12/Generative-AI-and-Disinformation_-White-Paper-v8.pdf.

Tailored untruths: How personalisation challenges LLM safeguards URL: https://edmo.eu/wp-content/uploads/2023/12/Generative-AI-and-Disinformation_-White-Paper-v8.pdf

Reference 2024

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Observation 73c650fd-10fe-4ed3-826f-b35e35458098 · outbound

This paper cites LLM Social Simulations Are a Promising Research Method.

Tailored untruths: How personalisation challenges LLM safeguards LLM Social Simulations Are a Promising Research Method

Reference 2025

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Observation c2e3d8e1-d377-4ee6-aecb-600965ee6894 · outbound

This paper cites Information Processing & Management 63, 104342.

Tailored untruths: How personalisation challenges LLM safeguards Information Processing & Management 63, 104342

Reference 2026

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Observation db692135-dd9a-432b-a7a3-5786e154f751 · outbound

This paper cites Mazeika, M., Phan, L., Yin, X., Zou, A., Wang, Z., Mu, N., Sakhaee, E., Li, N., Basart, S., Li, B., Forsyth, D., Hendrycks, D.,.

Tailored untruths: How personalisation challenges LLM safeguards Mazeika, M., Phan, L., Yin, X., Zou, A., Wang, Z., Mu, N., Sakhaee, E., Li, N., Basart, S., Li, B., Forsyth, D., Hendrycks, D.,

Reference 4692

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