SYNDICOM: Improving Conversational Commonsense with Error-Injection and
  Natural Language Feedback
Christopher Richardson Anirudh Sundar Larry Heck
Commonsense reasoning is a critical aspect of human communication. Despite
recent advances in conversational AI driven by large language models,
commonsense reasoning remains a challenging task. In this work, we introduce
SYNDICOM - a method for improving commonsense in dialogue response generation.
SYNDICOM consists of two components. The first component is a dataset composed
of commonsense dialogues created from a knowledge graph and synthesized into
natural language. This dataset includes both valid and invalid responses to
dialogue contexts, along with natural language feedback (NLF) for the invalid
responses. The second contribution is a two-step procedure: training a model to
predict natural language feedback (NLF) for invalid responses, and then
training a response generation model conditioned on the predicted NLF, the
invalid response, and the dialogue. SYNDICOM is scalable and does not require
reinforcement learning. Empirical results on three tasks are evaluated using a
broad range of metrics. SYNDICOM achieves a relative improvement of 53% over
ChatGPT on ROUGE1, and human evaluators prefer SYNDICOM over ChatGPT 57% of the
time. We will publicly release the code and the full dataset.