  Large language models (LLMs) such as GPT-3 have demonstrated a strong
capability to generate coherent and contextually relevant text. However, amidst
their successes, a crucial issue persists: their generated outputs still lack
commonsense at times. Moreover, fine-tuning the entire LLM towards more
commonsensical outputs is computationally expensive if not infeasible. In this
paper, we present a computation-efficient framework that steers a frozen
Pre-Trained Language Model (PTLM) towards more commonsensical generation (i.e.,
producing a plausible output that incorporates a list of concepts in a
meaningful way). Specifically, we first construct a reference-free evaluator
that assigns a sentence with a commonsensical score by grounding the sentence
to a dynamic commonsense knowledge base from four different relational aspects.
We then use the scorer as the oracle for commonsense knowledge, and extend the
controllable generation method called NADO to train an auxiliary head that
guides a fixed PTLM to better satisfy the oracle. We test our framework on a
series of GPT-2-, Flan-T5-, and Alpaca-based language models (LMs) on two
constrained concept-to-sentence benchmarks. Human evaluation results
demonstrate that our method consistently leads to the most commonsensical
outputs.
