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.
6613709b9844031543076487
Makesh Narsimhan Sreedhar
6613709b9844031543076488
Traian Rebedea
claimed_verified
2024-04-09T19:23:13.050Z
639712c17dca3977ed053eb0
https://cdn-avatars.huggingface.co/v1/production/uploads/1672930181422-639712c17dca3977ed053eb0.jpeg
Traian Rebedea
user
trebedea
6613709b9844031543076489
Shaona Ghosh
6613709b984403154307648a
Christopher Parisien
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.
The CantTalkAboutThis dataset improves language models' ability to stay on topic during conversations by including distractor turns, making them more resilient and coherent compared to general-purpose instruction-tuned models.
instruction-tuning datasets task-oriented interactions synthetic dialogues distractor turns topical coherence general-purpose instruction-tuned LLMs