CantTalkAboutThis: Aligning Language Models to Stay on Topic in Dialogues
<|reference_start|>CantTalkAboutThis: Aligning Language Models to Stay on Topic in Dialogues: 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, including safety alignment.<|reference_end|>
@article{sreedhar2024canttalkaboutthis:,
    title={CantTalkAboutThis: Aligning Language Models to Stay on Topic in
  Dialogues},
    author={Makesh Narsimhan Sreedhar, Traian Rebedea, Shaona Ghosh, Jiaqi Zeng
  and Christopher Parisien},
    journal={arXiv preprint arXiv:2404.03820},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.03820},
    primaryClass={cs.CL}
}