Systematic Analysis and Induction of Character Hallucination in Role-Playing Language Models

Conceptos Básicos

Character hallucination, where language models deviate from predefined character roles and generate inconsistent responses, is a persistent issue in role-playing systems. This phenomenon can be systematically analyzed and induced through the RoleBreak framework, which identifies query sparsity and role-query conflict as the key drivers of hallucination.

Resumen

The paper presents a comprehensive analysis of character hallucination in large language model (LLM)-based role-playing systems. It introduces the RoleBreak framework, which identifies two core mechanisms behind character hallucination:

1. Query Sparsity: Due to the limited coverage of role-related queries in training datasets, certain queries fall outside the expected distribution, leading to the model's failure to respond correctly.

2. Role-Query Conflict: Conflicts between the role-setting instructions and user queries cause the model to struggle in managing these contradictions, failing to meet the user's demand for creative content.

The authors construct the RoleBreakEval dataset using a semi-automated pipeline based on these two principles, and the quantitative evaluation confirms the feasibility of rapidly inducing character hallucination through simple methods. The results reveal that even advanced LLMs trained with extensive role-playing enhancements remain vulnerable to RoleBreak attacks.

To address these vulnerabilities, the authors propose a novel defense mechanism called the "Narrator Mode". This approach generates supplementary narrative context to improve the model's ability to generalize across diverse queries and resolve conflicts between role instructions and user queries, while enhancing the coherence of the overall story in role-playing interactions. Experimental results demonstrate that Narrator Mode significantly outperforms traditional refusal-based strategies in reducing hallucinations, improving fidelity to character roles and queries, and enhancing overall narrative coherence.

Estadísticas

Role-playing systems powered by large language models (LLMs) are susceptible to character hallucinations, where the model deviates from predefined character roles and generates inconsistent responses.
Two core mechanisms driving character hallucination are query sparsity and role-query conflict.
The RoleBreakEval dataset is constructed using a semi-automated pipeline based on these two principles.
Quantitative evaluation confirms the feasibility of rapidly inducing character hallucination through simple methods.
Even advanced LLMs trained with extensive role-playing enhancements remain vulnerable to RoleBreak attacks.

Citas

"Character hallucination refers to the phenomenon where the model generates responses inconsistent with the character's identity or knowledge."
"We argue that these two factors are the fundamental causes of character hallucination, which we collectively term the RoleBreak."
"To address these vulnerabilities, we propose a novel defence strategy, the Narrator Mode, which generates supplemental narrative context to mitigate role-query conflicts and improve query generalization."

Ideas clave extraídas de

RoleBreak: Character Hallucination as a Jailbreak Attack in Role-Playing Systems

by Yihong Tang,... a las arxiv.org 09-26-2024

https://arxiv.org/pdf/2409.16727.pdf

Consultas más profundas

How can the RoleBreak framework be extended to other types of language models beyond role-playing systems, such as task-oriented dialogue systems or open-domain chatbots?

The RoleBreak framework, which identifies character hallucination in role-playing systems, can be effectively adapted to other types of language models, including task-oriented dialogue systems and open-domain chatbots. This extension can be achieved by focusing on the core mechanisms of query sparsity and role-query conflict, which are relevant across various dialogue contexts.

Query Sparsity: In task-oriented dialogue systems, the framework can analyze the diversity of user queries and the model's ability to handle them. By identifying gaps in the training data where certain task-related queries are underrepresented, developers can enhance the model's training datasets to include a broader range of scenarios. This could involve generating synthetic queries that simulate real-world user interactions, thereby improving the model's generalization capabilities.

Role-Query Conflict: For open-domain chatbots, the RoleBreak framework can be utilized to examine conflicts between user expectations and the chatbot's predefined knowledge or persona. By systematically introducing conflicting queries, developers can assess how well the chatbot reconciles these conflicts. This could lead to the development of more sophisticated response strategies that balance adherence to the chatbot's identity with the need to provide relevant and engaging responses.

Cross-Domain Adaptation: The principles of RoleBreak can also be applied to enhance cross-domain capabilities in dialogue systems. By analyzing how models respond to queries that span multiple domains, developers can create hybrid models that leverage knowledge from various fields, thus reducing hallucinations and improving user satisfaction.

What are the potential ethical implications of character hallucination in role-playing systems, and how can they be addressed through technical and non-technical means?

Character hallucination in role-playing systems poses several ethical implications that can affect user experience and trust in AI systems. These implications include:

Misinformation: When a model generates responses that deviate from the established character identity, it can lead to the dissemination of inaccurate information, particularly if the character is based on historical figures or well-known personalities. This can mislead users and distort their understanding of the character's true nature.

User Manipulation: If a role-playing system fails to maintain character fidelity, it may inadvertently manipulate user emotions or perceptions, especially in sensitive scenarios. This can lead to negative experiences, particularly if users are emotionally invested in the narrative.

Cultural Sensitivity: Character hallucination can result in responses that are culturally insensitive or inappropriate, especially when dealing with characters from diverse backgrounds. This can alienate users and perpetuate stereotypes.

To address these ethical implications, both technical and non-technical measures can be implemented:

Technical Measures:

Robust Training: Enhance training datasets to include diverse and representative character portrayals, ensuring that models are less likely to generate hallucinated responses.
Real-time Monitoring: Implement monitoring systems that flag potentially inappropriate or inaccurate responses, allowing for real-time corrections or user warnings.
User Feedback Mechanisms: Incorporate user feedback loops that allow users to report hallucinations, which can be used to refine the model and improve its accuracy over time.

Non-Technical Measures:

User Education: Educate users about the limitations of AI in role-playing systems, emphasizing that responses may not always align with character expectations.
Ethical Guidelines: Establish ethical guidelines for developers to follow when creating role-playing systems, focusing on cultural sensitivity and accuracy in character representation.
Community Engagement: Involve diverse communities in the development process to ensure that character portrayals are respectful and accurate, fostering a more inclusive environment.

Given the importance of narrative coherence in role-playing interactions, how can the Narrator Mode be further enhanced to generate more nuanced and contextually-aware supplementary descriptions that better capture the emotional and thematic aspects of the story?

To enhance the Narrator Mode for generating more nuanced and contextually-aware supplementary descriptions in role-playing interactions, several strategies can be employed:

Emotion Recognition: Integrate advanced emotion recognition algorithms that analyze user inputs and the context of the dialogue. By understanding the emotional tone of user queries, the Narrator Mode can tailor its supplementary descriptions to reflect the appropriate emotional responses of the character, thereby enriching the narrative experience.

Thematic Analysis: Implement thematic analysis tools that identify underlying themes in the dialogue. By recognizing recurring motifs or themes, the Narrator Mode can generate descriptions that resonate with these themes, creating a more cohesive and immersive storytelling experience.

Dynamic Contextualization: Develop a dynamic contextualization mechanism that adjusts supplementary descriptions based on the evolving narrative. This could involve maintaining a memory of past interactions and user preferences, allowing the Narrator Mode to provide contextually relevant details that enhance the storyline's depth and continuity.

User Personalization: Incorporate user personalization features that adapt the narrative style and content based on individual user preferences. By analyzing user interactions over time, the Narrator Mode can learn to generate descriptions that align with the user's interests and storytelling preferences, fostering a more engaging experience.

Collaborative Storytelling: Facilitate collaborative storytelling by allowing users to contribute to the narrative actively. The Narrator Mode can prompt users for input on key plot points or character decisions, generating supplementary descriptions that reflect this collaboration and enhance the overall narrative coherence.

By implementing these enhancements, the Narrator Mode can significantly improve its ability to generate rich, emotionally resonant, and thematically coherent supplementary descriptions, ultimately leading to a more satisfying role-playing experience for users.