Data diversity trumps quantity in LLM instruction-following generalization.
Cross-domain instruction variety key to LLM generalization and adaptability.
https://arxiv.org/abs/2410.04717
Original Problem 🔍:
Instruction-following capabilities in LLMs are crucial but poorly understood. Existing research lacks systematic analysis of factors influencing generalization to unseen instructions.
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Solution in this Paper 🧠:
• Introduces controlled string-rewriting tasks to isolate instruction-following abilities
• Examines impact of instruction diversity on model generalization
• Analyzes real-world scenarios: specialist (code generation) and generalist LLM fine-tuning
• Proposes strategic data diversification for improved performance
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Key Insights from this Paper 💡:
• Generalization emerges only with sufficient cross-domain instruction diversity
• Data diversity matters more than quantity for improving model performance
• Optimal performance requires balancing specialization and diversification
• Strategic data curation enhances both specialist and generalist LLM capabilities
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Results 📊:
• Generalization accuracy improves sharply with 300-1000 unique instructions
• Cross-domain diversification boosts code generation performance by 3%
• Diverse instruction mixtures enhance generalist LLM capabilities across tasks
• Balanced specialization and diversification yield 14.06% relative gain in generalist scenarios
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