PingPong leverages language models to emulate users and judges in role-playing scenarios for LLM evaluation.
📚 https://arxiv.org/abs/2409.06820
Original Problem 🎭:
Existing benchmarks for evaluating role-playing language models lack dynamic, multi-turn interactions and are vulnerable to data contamination. Static datasets and single-turn evaluations fail to capture the complexity of real-world role-playing scenarios.
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Key Insights from this Paper 💡:
• Language models can emulate users and evaluate conversations, reducing reliance on human annotators
• Multi-model evaluation mitigates individual biases
• Dynamic test data generation prevents contamination
• Asymmetrical setup mirrors real-world role-playing scenarios
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Solution in this Paper 🔬:
• PingPong benchmark with three components:
- Player model: assumes character role
- Interrogator model: simulates user behavior
- Judge model: evaluates conversation quality
• Uses Claude 3.5 Sonnet and GPT-4 for evaluation
• Implements 5-point Likert scale for scoring
• Evaluates character consistency, entertainment value, and language fluency
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Results 📊:
• Strong correlations with human annotations:
- English: 0.647 (averaged final score)
- Russian: 0.669 (averaged final score)
• Best open-source models:
- English: Llama 3.1 70B
- Russian: Gemma 2 Ataraxy 9B
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📚 https://arxiv.org/abs/2409.06820
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