Alignment tuning has become the de facto standard practice for enabling base large language models (LLMs)  to serve as open-domain AI assistants.
The alignment tuning process typically involves instruction learning through supervised fine-tuning (SFT)  and preference tuning via reinforcement learning from human feedback (RLHF) .
A recent study, LIMA, shows that using merely 1K examples for SFT can achieve significant alignment performance as well, suggesting that the effect of alignment tuning might be “superficial. ”
This raises questions abouthow exactly the alignment tuning transforms a base LLM. We analyze the effect of alignment tuning by examining the token distribution shift between base LLMs and their aligned counterpart (e.g., Llama-2 and Llama-2-chat) .
Our findings reveal that base LLMs and their alignment-tuned versions perform nearly identically in decoding on the majority of token positions (i.e., they share the top-ranked tokens) .
Most distribution shifts occur withstylistictokens (e.g., discourse markers, safety disclaimers) . These direct evidence strongly supports the hypothesis that alignment tuning primarily learns to adopt the language style of AI assistants, and that the knowledge required for answering user queries predominantly comes from the base LLMs themselves. Based on these findings, we rethink the alignment of LLMs by posing the research question:how effectively can we align base LLMs without SFT or RLHF?To address this,
we introduce a simple, tuning-free alignment method,Urial(Untuned LLMs withRestyledIn-contextALignment) .Urialachieves effective alignment purely through in-context learning (ICL)  with base LLMs, requiring as few as three constant stylistic examples and a system prompt.
We conduct a fine-grained and interpretable evaluation on a diverse set of examples, namedjust-eval-instruct.
Results demonstrate that base LLMs withUrialcan match or even surpass the performance of LLMs aligned with SFT (Mistral-7b-Instruct)  or SFT+RLHF (Llama-2-70b-chat) .
We show that the gap between tuning-free and tuning-based alignment methods can be significantly reduced through strategic prompting and ICL.
Our findings on superficial nature of alignment tuning and results withUrialsuggest that deeper analysis and theoretical understanding of alignment is crucial to future LLM research.