CantTalkAboutThis: Aligning Language Models to Stay on Topic in Dialogues

• URL: http://arxiv.org/abs/2404.03820v2
• Date: Fri, 21 Jun 2024 13:57:11 GMT
• Title: CantTalkAboutThis: Aligning Language Models to Stay on Topic in Dialogues
• Authors: Makesh Narsimhan Sreedhar, Traian Rebedea, Shaona Ghosh, Jiaqi Zeng, Christopher Parisien,
• Abstract summary: CantTalkAboutThis dataset consists of synthetic dialogues on a wide range of conversation topics from different domains. Fine-tuning language models on this dataset helps make them resilient to deviating from the role assigned. Preliminary observations suggest that training models on this dataset also enhance their performance on fine-grained instruction following tasks, including safety alignment.
• Score: 4.427811636536821
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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