  We introduce Aligner, a novel Parameter-Efficient Fine-Tuning (PEFT) method
for aligning multi-billion-parameter-sized Large Language Models (LLMs).
Aligner employs a unique design that constructs a globally shared set of
tunable tokens that modify the attention of every layer. Remarkably with this
method, even when using one token accounting for a mere 5,000 parameters,
Aligner can still perform comparably well to state-of-the-art LLM adaptation
methods like LoRA that require millions of parameters. This capacity is
substantiated in both instruction following and value alignment tasks. Besides
the multiple order-of-magnitude improvement in parameter efficiency, the
insight Aligner provides into the internal mechanisms of LLMs is also valuable.
The architectural features and efficacy of our method, in addition to our
experiments demonstrate that an LLM separates its internal handling of "form"
and "knowledge" in a somewhat orthogonal manner. This finding promises to
motivate new research into LLM mechanism understanding and value alignment.
