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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 12·5 min read
How LLMs Follow Instructions: Skillful Coordination, Not a Universal Mechanism
TL;DR
Instruction tuning in language models does not rely on a universal mechanism for following instructions, but rather on skillful coordination of diverse linguistic capabilities. This study reveals that instruction-following is structured and task-specific, challenging the notion of a one-size-fits-all approach.
✦ Why It Matters
Consider revising your approach to instruction tuning by focusing on task-specific skills rather than a universal mechanism.
Key Takeaways
- Explore task-specific strategies for instruction tuning.
- Utilize diagnostic probing to assess model capabilities.
- Focus on skill similarity when designing cross-task applications.
- Analyze complexity stratification in tasks for better model performance.
How It Works
The study employs diagnostic probing to evaluate how well instruction-tuned models perform across various tasks. It reveals that these models do not share a universal representation but instead coordinate different linguistic skills based on task requirements.
The analysis shows that simpler tasks are processed earlier in the model's layers, while more complex semantic tasks are handled later, indicating a structured approach to instruction-following.