  Aligning large language models (LLMs) with human preferences is essential for
safe and useful LLMs. Previous works mainly adopt reinforcement learning (RLHF)
and direct preference optimization (DPO) with human feedback for alignment.
Nevertheless, they have certain drawbacks. One such limitation is that they can
only align models with one preference at the training time (e.g., they cannot
learn to generate concise responses when the preference data prefers detailed
responses), or have certain constraints for the data format (e.g., DPO only
supports pairwise preference data). To this end, prior works incorporate
controllable generations for alignment to make language models learn multiple
preferences and provide outputs with different preferences during inference if
asked. Controllable generation also offers more flexibility with regard to data
format (e.g., it supports pointwise preference data). Specifically, it uses
different control tokens for different preferences during training and
inference, making LLMs behave differently when required. Current controllable
generation methods either use a special token or hand-crafted prompts as
control tokens, and optimize them together with LLMs. As control tokens are
typically much lighter than LLMs, this optimization strategy may not
effectively optimize control tokens. To this end, we first use
parameter-efficient tuning (e.g., prompting tuning and low-rank adaptation) to
optimize control tokens and then fine-tune models for controllable generations,
similar to prior works. Our approach, alignMEnt with parameter-Efficient Tuning
(MEET), improves the quality of control tokens, thus improving controllable
generation quality consistently by an apparent margin on two well-recognized
datasets compared with prior works.
