Concept Incongruence: An Exploration of Time and Death in Role Playing
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Abstract (arXiv)
Consider this prompt "Draw a unicorn with two horns". Should large language models (LLMs) recognize that a unicorn has only one horn by definition and ask users for clarifications, or proceed to generate something anyway? We introduce concept incongruence to capture such phenomena where concept boundaries clash with each other, either in user prompts or in model representations, often leading to under-specified or mis-specified behaviors. In this work, we take the first step towards defining and analyzing model behavior under concept incongruence. Focusing on temporal boundaries in the Role-Play setting, we propose three behavioral metrics--abstention rate, conditional accuracy, and answer rate--to quantify model behavior under incongruence due to the role's death. We show that models fail to abstain after death and suffer from an accuracy drop compared to the Non-Role-Play setting. Through probing experiments, we identify two main causes: (i) unreliable encoding of the "death" state across different years, leading to unsatisfactory abstention behavior, and (ii) role playing causes shifts in the model's temporal representations, resulting in accuracy drops. We leverage these insights to improve consistency in the model's abstention and answer behaviors. Our findings suggest that concept incongruence leads to unexpected model behaviors and point to future directions on improving model behavior under concept incongruence.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 40 of its 53 references that are in the map. First posted on arXiv 20 May 2025.
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40 of 53 references.
References · 40
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
- 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
- 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
- 2022Locating and Editing Factual Associations in GPTcited by 2,691
- 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
- 2022Survey of Hallucination in Natural Language Generationcited by 2,075
- 2018CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledgecited by 1,822
- 2023Jailbroken: How Does LLM Safety Training Fail?cited by 1,651
- 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,601
- 2024A Survey on LLM-as-a-Judgecited by 1,423
- 2016Understanding intermediate layers using linear classifier probescited by 1,353
28 more
- 2023ChatDev: Communicative Agents for Software Developmentcited by 919
- 2021Probing Classifiers: Promises, Shortcomings, and Advancescited by 876
- 2023In-Context Retrieval-Augmented Language Modelscited by 702
- 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 698
- 2023Active Retrieval Augmented Generationcited by 516
- 2024From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judgecited by 494
- 2023Editing Large Language Models: Problems, Methods, and Opportunitiescited by 372
- 2023A Survey of Hallucination in Large Foundation Modelscited by 368
- 2023SemDeDup: Data-efficient learning at web-scale through semantic deduplicationcited by 303
- 2021Time-Aware Language Models as Temporal Knowledge Basescited by 296
- 2023Role-Play with Large Language Modelscited by 294
- 2021A Practical Guide to Multi-Objective Reinforcement Learning and Planningcited by 256
- 2021Mind the Gap: Assessing Temporal Generalization in Neural Language Modelscited by 230
- 2023Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMscited by 199
- 2023LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examplescited by 177
- 2023RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Modelscited by 175
- 2021A Dataset for Answering Time-Sensitive Questionscited by 154
- 2021SituatedQA: Incorporating Extra-Linguistic Contexts into QAcited by 145
- 2024CharacterEval: A Chinese Benchmark for Role-Playing Conversational Agent Evaluationcited by 143
- 2022TimeLMs: Diachronic Language Models from Twittercited by 131
- 2021Time Waits for No One! Analysis and Challenges of Temporal Misalignmentcited by 91
- 2024Knowledge Mechanisms in Large Language Models: A Survey and Perspectivecited by 51
- 2023CharacterGLM: Customizing Chinese Conversational AI Characters with Large Language Modelscited by 34
- 2025The Law of Knowledge Overshadowing: Towards Understanding, Predicting, and Preventing LLM Hallucinationcited by 23
- 2024TimeChara: Evaluating Point-in-Time Character Hallucination of Role-Playing Large Language Modelscited by 22
- 2022Lexicographic Multi-Objective Reinforcement Learningcited by 17
- 2023VIBE: Topic-Driven Temporal Adaptation for Twitter Classificationcited by 12
- 2024PingPong: A Benchmark for Role-Playing Language Models with User Emulation and Multi-Model Evaluationcited by 10
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