title: CantTalkAboutThis: Aligning Language Models to Stay on Topic in
    Dialogues
summary: 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.
paper_authors._id: 6613709b9844031543076487
paper_authors.name: Makesh Narsimhan Sreedhar
paper_authors._id: 6613709b9844031543076488
paper_authors.name: Traian Rebedea
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-04-09T19:23:13.050Z
paper_authors._id: 639712c17dca3977ed053eb0
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1672930181422-639712c17dca3977ed053eb0.jpeg
paper_authors.fullname: Traian Rebedea
paper_authors.type: user
paper_authors.user: trebedea
paper_authors._id: 6613709b9844031543076489
paper_authors.name: Shaona Ghosh
paper_authors._id: 6613709b984403154307648a
paper_authors.name: Christopher Parisien
paper_title: CantTalkAboutThis: Aligning Language Models to Stay on Topic in
    Dialogues
paper_summary: 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.
paper_ai_keywords: instruction-tuning datasets mathematical reasoning logical reasoning language models chatbots CantTalkAboutThis dataset synthetic dialogues distractor turns fine-tuning topic relevance topical coherence GPT-4-turbo Mixtral-Instruct fine-grained instruction following tasks