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.

我们提出了一种计算高效的框架，通过引导一个固定的预训练语言模型向更具常识的生成方式迈进，以产生一个合理的输出，该输出以一系列概念有意义的方式融合。测试结果表明，我们的方法在两个有限概念生成句子的基准上始终能够产生最具常识性的输出。