RoleInteract: Evaluating Social Interaction of Role-Playing Agents

核心概念

RoleInteract introduces a benchmark to evaluate the social interaction of role-playing conversational agents, highlighting the importance of assessing both individual and group levels.

要約

RoleInteract evaluates social intelligence in role-playing agents, emphasizing individual and group dynamics. The benchmark includes diverse sources, covering 500 characters with 6,000 questions and 30,800 utterances. Evaluation metrics focus on self-awareness, emotional perception, conversation memory, and social preference. Mainstream LLMs are assessed for performance.
Introduction:

Large language models have enhanced AI conversational agents.
Role-specific knowledge evaluation is crucial.
RoleInteract aims to assess social intelligence in role-playing agents.
Individual Level Assessment:

Self-awareness on role description is essential for consistency.
Emotional perception aids in understanding others' emotions.
Long-term conversation memory enhances reliability.
Group Level Assessment:

Social preference towards group dynamics influences behavior.
Complex interactions within groups impact agent behavior.
Data Construction:

Dialogue construction methods ensure fluency and fidelity.
Question design tailored to dimensions like self-awareness and emotional perception.
Experiment Settings:

Metrics like accuracy and keyword coverage rate used for evaluation.
Results and Analysis:

Closed-source models outperform open-source ones.
Performance declines with longer conversations and complex group dynamics.
Conclusion:

RoleInteract provides a comprehensive evaluation framework for assessing sociality in role-playing agents at individual and group levels.

統計

Large language models have advanced the development of various AI conversational agents.
The benchmark covers 500 characters with over 6,000 question prompts and 30,800 multi-turn role-playing utterances.
Agents excelling at the individual level may not perform well at the group level.

引用

抽出されたキーインサイト

RoleInteract

by Hongzhan Che... 場所 arxiv.org 03-21-2024

https://arxiv.org/pdf/2403.13679.pdf

深掘り質問

How can RoleInteract's findings be applied to improve real-world applications of role-playing conversational agents?

RoleInteract's findings provide valuable insights into the social intelligence of role-playing conversational agents, highlighting their strengths and weaknesses in individual and group interactions. By analyzing the performance of various LLMs on RoleInteract, developers can identify areas for improvement in these agents. For instance, if an LLM excels at individual-level tasks but struggles with group dynamics, developers can focus on enhancing its ability to navigate complex social interactions within a group setting. Additionally, RoleInteract's evaluation metrics can serve as benchmarks for assessing the effectiveness of different models and guiding future research efforts towards creating more socially intelligent role-playing agents.

What potential biases or limitations could affect the accuracy of evaluations conducted by mainstream LLMs on RoleInteract?

Several biases and limitations could impact the accuracy of evaluations conducted by mainstream LLMs on RoleInteract. One potential bias is model-specific bias, where certain LLMs may perform better or worse based on their design or training data. This bias could lead to skewed results that do not accurately reflect a model's true capabilities. Another limitation is dataset bias, where the composition of RoleInteract may favor certain types of characters or scenarios over others, influencing how well models perform across different dimensions. Moreover, human annotator bias during dataset construction and validation processes could introduce subjective judgments that affect evaluation outcomes.

How might the study of social intelligence in role-playing agents contribute to advancements in human-computer interaction research?

Studying social intelligence in role-playing agents offers significant contributions to advancements in human-computer interaction (HCI) research. Understanding how these agents perceive and respond to social cues can enhance user engagement and satisfaction when interacting with AI systems. By improving an agent's ability to mimic human behaviors accurately through nuanced social interactions, HCI researchers can create more natural and effective communication interfaces between humans and machines. Additionally, insights from studying social intelligence in role-playing agents can inform the development of AI-driven applications across various domains such as customer service chatbots, virtual assistants, educational tools, and entertainment platforms.