CantTalkAboutThis: Aligning Language Models to Stay on Topic in
  Dialogues
Makesh Narsimhan Sreedhar Traian Rebedea Shaona Ghosh Christopher Parisien
Recent advancements in instruction-tuning datasets have predominantly focused
on specific tasks like mathematical or logical reasoning. There has been a
notable gap in data designed for aligning language models to maintain topic
relevance in conversations - a critical aspect for deploying chatbots to
production. We introduce the CantTalkAboutThis dataset to help language models
remain focused on the subject at hand during task-oriented interactions. It
consists of synthetic dialogues on a wide range of conversation topics from
different domains. These dialogues are interspersed with distractor turns that
intentionally divert the chatbot from the predefined topic. Fine-tuning
language models on this dataset helps make them resilient to deviating from the
role assigned and improves their ability to maintain topical coherence compared
to general-purpose instruction-tuned LLMs like GPT-4-turbo and
Mixtral-Instruct. Additionally, preliminary observations suggest that training
models on this dataset also enhance their performance on fine-grained
instruction following tasks.