  While supervised fine-tuning (SFT) has been a straightforward approach for
tailoring the output of foundation large language model (LLM) to specific
preferences, concerns have been raised about the depth of this alignment, with
some critiques suggesting it is merely "superficial". We critically examine
this hypothesis within the scope of cross-lingual generation tasks, proposing
that the effectiveness of SFT may be constrained by its reliance on prior
tokens to guide cross-lingual generation. Based on this crucial insight, and in
response to the challenges posed by the costly and limited availability of
non-English data for SFT, we introduce a novel training-free alignment method
named PreTTY, which employs minimal task-related prior tokens to bridge the
foundation LLM and the SFT LLM, achieving comparable performance without
training. Experiments on machine translation and part-of-speech tagging across
eight languages demonstrate the efficacy of PreTTY in cross-lingual settings.
Remarkably, by initiating the decoding process with only one or two prior
tokens, foundation LLMs can achieve performance comparable to their SFT
counterparts. This method presents a cost-effective alternative to SFT and
advances the democratization of multilingual LLMs.
