Wide Language Model (LLM) agents are experiencing rapid diversification in their applications, ranging from customer support chatbots to code generation and robotics. This expanding scope has created a pressing need to tailor these agents to align with diverse user specifications, enabling highly personalized experiences across various applications and user bases. The primary challenge lies in developing LLM agents that can effectively embody specific personas, allowing them to generate output that accurately reflects the personality, experiences, and knowledge associated with their assigned roles. This customization is crucial to creating more engaging, contextually appropriate, and user-personalized interactions in an increasingly diverse digital landscape.
Researchers have repeatedly attempted to address the challenges presented by creating effective personality agents. One approach involves using datasets with predetermined personalities to initialize these agents. However, this method significantly restricts the evaluation of personalities not included in the datasets. Another approach focuses on initializing personality agents in multiple relevant environments, but this often fails to provide a comprehensive assessment of the agent's capabilities. Existing evaluation benchmarks such as RoleBench, InCharacter, CharacterEval, and RoleEval have been developed to assess the role-playing abilities of LLMs. These benchmarks use various methods, including GPT-generated QA pairs, psychological scales, and multiple-choice questions. However, they often evaluate personality agents along a single skill axis, such as language capabilities or decision-making, and do not provide comprehensive information about all dimensions of an LLM agent's interactions when assuming a personality.
Researchers from Carnegie Mellon University, the University of Illinois Chicago, the University of Massachusetts Amherst, Georgia tech, Princeton University, and an independent researcher present Gym Person A dynamic assessment framework for personality agents. Assesses capabilities across multiple dimensions and environments relevant to assigned personalities. The process begins with an LLM reasoner selecting appropriate configurations from 150 different environments, followed by the generation of task-specific questions. PersonaGym introduces PersonaScore, a robust automatic metric for assessing agents’ overall capabilities across diverse environments. This metric uses rubrics curated by experts and LLM reasoners to provide calibrated example responses. It then employs multiple state-of-the-art LLM rater models, combining their scores to comprehensively assess agents’ responses. This approach enables large-scale automated assessment for any personality in any environment, providing a more robust and versatile method for developing and assessing personality agents.
PersonaGym is a dynamic assessment framework for personality agents that evaluates their performance on five key tasks in relevant environments. The framework consists of several interconnected components that work together to provide a comprehensive assessment:
- Dynamic environment selection: An LLM reasoner chooses appropriate environments from a set of 150 options based on the agent's personality description.
- Question Generation: For each assessment task, an LLM reasoner creates 10 task-specific questions per selected environment, designed to assess the agent's ability to respond according to its personality.
- Character Agent Response Generation: The LLM agent assumes the given character using a system-specific message and responds to the generated questions.
- Reasoning examples: The evaluation rubrics are enriched with example answers for each possible score (1-5), adapted to each person-question pair.
- Joint Assessment: Two state-of-the-art LLM assessment models evaluate each agent's response using comprehensive rubrics and generating scores with justifications.
This multi-step process enables PersonaGym to provide a nuanced, context-aware assessment of personal agents, addressing the limitations of previous approaches and offering a more comprehensive assessment of agent capabilities in a variety of environments and tasks.
The performance of personality agents varies significantly across tasks and models. Action justification and personality consistency show the most variability, while linguistic habits emerge as the most challenging task for all models. No model consistently excels across all tasks, highlighting the need for multidimensional evaluation. Model size generally correlates with better performance, as seen in LLaMA 2's progression from 13b to 70b. Surprisingly, LLaMA 3 (8b) outperforms larger models on most tasks. Claude 3 Haiku, despite being state-of-the-art, shows reluctance to adopt personalities.
PersonaGym is an innovative framework for evaluating character agents across multiple tasks using dynamically generated questions. It initializes agents in relevant environments and evaluates them on five decision-theoretic-based tasks. The framework introduces PersonaScore, which measures a LLM’s proficiency in role-playing. Benchmarking 6 LLMs on 200 characters reveals that model size does not necessarily correlate with improved character agent performance. The study highlights discrepancies in improvement between advanced and less capable models, emphasizing the need for innovation in character agents. Correlation testing demonstrates PersonaGym’s strong alignment with human assessments, validating its effectiveness as a comprehensive assessment tool.
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Asjad is a consultant intern at Marktechpost. He is pursuing Bachelors in Mechanical Engineering from Indian Institute of technology, Kharagpur. Asjad is a Machine Learning and Deep Learning enthusiast who is always researching the applications of Machine Learning in the healthcare domain.
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