Alignment tuning, the process of fine-tuning large language models (LLMs) for AI assistants, may have a superficial effect. Token distribution analysis shows that alignment tuning primarily learns language style, while base LLMs provide the knowledge for answering queries. A tuning-free alignment method, URIAL, achieves effective alignment with as few as three stylistic examples and a prompt. URIAL can match or surpass the performance of tuning-based methods. Deeper analysis and understanding of alignment are needed for future LLM research.

https://arxiv.org/abs//2312.01552

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