Enterprise AI Analysis
COSER: Coordinating LLM-Based Persona Simulation of Established Roles
Role-playing language agents (RPLAs) hold immense promise, yet simulating established characters accurately remains a formidable challenge. This paper introduces COSER, a groundbreaking solution addressing the critical gaps in authentic character datasets and nuanced evaluation. COSER provides a rich dataset from 771 renowned books, open-source models, and a novel Given-Circumstance Acting (GCA) evaluation protocol. Our COSER 70B model achieves state-of-the-art performance, outperforming or matching GPT-4o on various benchmarks, demonstrating the power of high-fidelity data and advanced simulation.
Executive Impact: Key Metrics
COSER's innovative approach dramatically improves LLM-based persona simulation, delivering unprecedented accuracy and fidelity across diverse characters from literature.
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LifeChoice Accuracy (CoSER 70B)
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Unique Characters Modeled
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Renowned Books Sourced
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Evaluation Alignment with Humans
Deep Analysis & Enterprise Applications
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Given-Circumstance Acting (GCA) Framework
COSER introduces Given-Circumstance Acting (GCA), a novel methodology for training and evaluating Role-Playing Language Agents (RPLAs). Inspired by Stanislavski's acting theory, GCA enables LLMs to sequentially portray multiple characters within authentic book scenes, capturing nuanced personalities and complex backgrounds. This approach ensures faithful alignment with character personas by leveraging comprehensive contextual data.
Enterprise Process Flow
Book Content Acquisition
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LLM-based Data Extraction
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COSER Dataset Creation
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GCA Model Training
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Multi-Agent Simulation
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Penalty-based LLM Judging
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Refined RPLA Performance
COSER Dataset: Richness & Authenticity
The COSER dataset stands out by extracting authentic, multi-character dialogues and comprehensive data directly from 771 acclaimed literary works, ensuring high source fidelity. Unlike LLM-synthesized datasets, COSER includes not just dialogues, but also conversation settings, plot summaries, character experiences, and crucial inner thoughts and actions, enabling more sophisticated persona simulations. This rich, contextual data is vital for training and evaluating RPLAs that truly understand and embody complex characters.
| Feature | Existing Datasets (Typical) | CoSER (Key Differentiators) |
|---|---|---|
| Source | LLM-Synthesized Q&A or limited human annotated | Authentic dialogues from 771 renowned books |
| Data Types | Profiles, basic dialogues | Profiles, structured experiences, plot summaries, conversations, inner thoughts, actions, environment context |
| Multi-Character Dialogues | Limited (mostly 2-char) | Extensive (many >2-char conversations) |
| Inner Thoughts/Actions | Rarely included | Explicitly extracted/inferred |
| Authenticity | Compromised by synthesis | High source fidelity |
Unprecedented RPLA Performance
COSER models, particularly COSER 70B, have achieved state-of-the-art performance across multiple RPLA benchmarks and our rigorous GCA evaluation. Leveraging authentic data and the GCA framework, these models demonstrate superior character portrayal and conversational dynamics. This section highlights key performance gains and the impact of crucial model components.
3.02%
Average Score Increase on CoSER Test (CoSER 70B trained with Inner Thoughts)
Our ablation studies reveal that including inner thoughts and motivations during training significantly improves LLMs' role-playing performance. For CoSER 70B, training with inner thoughts leads to a 3.02% increase in average scores on the CoSER Test, enabling more human-like and nuanced character portrayals.
2.15%
Average Score Increase with Retrieval (Expr+Conv, CoSER 70B)
Leveraging comprehensive data types from the COSER dataset for retrieval augmentation, such as character experiences and conversations, substantially boosts model performance. For CoSER 70B, combining experiences and conversations via retrieval augmentation leads to a 2.15% average score increase, indicating the critical role of external knowledge in grounding RPLAs' responses and actions, leading to more faithful character portrayals.
Transforming AI with Established Roles
COSER's unique dataset and GCA framework open new avenues for developing highly sophisticated role-playing language agents. This enables more realistic character chatbots, advanced agents in video games, and even digital clones for humans. The ability to simulate established characters with high fidelity, including their internal thoughts and actions, represents a significant step towards anthropomorphic cognition in AI.
See a direct application of COSER's capabilities in our featured case study below, demonstrating how COSER 70B faithfully captures complex character emotions.
Case Study: Cersei Lannister's Walk of Atonement
In a powerful demonstration of COSER 70B's advanced capabilities, we analyze its simulation of Cersei Lannister's 'Walk of Atonement' from A Dance with Dragons. While other leading models like GPT-4o and Claude-3.5-Sonnet often resort to stereotypical portrayals of Cersei's arrogance, COSER 70B accurately captures her suppressed anger and inner struggle, as depicted in the original narrative. This nuanced simulation highlights COSER's ability to model complex human cognition and emotional depth, reflecting true character fidelity beyond surface-level traits. This level of subtlety is crucial for enterprise applications requiring highly authentic and emotionally intelligent AI personas.
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Use our calculator to estimate the potential annual savings and reclaimed human hours by integrating advanced AI like COSER into your enterprise workflows.
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Your Enterprise AI Roadmap
A structured approach to integrating COSER's advanced RPLA capabilities into your organization.
Discovery & Strategy
Assess current workflows, identify key personas, and define objectives for RPLA integration. Tailor COSER's framework to align with specific enterprise needs.
Data Customization & Training
Utilize the COSER dataset as a foundation, or integrate proprietary data to fine-tune models for your specific character sets and industry contexts, ensuring high fidelity.
Model Deployment & Integration
Deploy COSER-based RPLA models into existing platforms (e.g., customer service, virtual assistants, gaming). Implement multi-agent simulation for complex interactions.
Performance Monitoring & Optimization
Leverage GCA evaluation protocols for continuous assessment of persona fidelity and dialogue quality. Iteratively refine models for peak performance and user experience.
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