Teaching chatbots to stop contradicting themselves (DECODE)

Kari Jaaskelainen

12 Nov 2025 — 1 min read

Teaching chatbots to stop contradicting themselves

Ever had a bot say one thing, then the opposite a few turns later? This study introduces DECODE—a new task and dataset for spotting contradictions in everyday conversations, drawn from both human-human and human-bot chats.

New data beats existing natural language inference (NLI) resources for training contradiction detectors in dialogue.
A structured, utterance-by-utterance approach using pre-trained Transformers outperforms typical unstructured methods, especially on tough, out-of-distribution chats.
The best model’s scores align well with human judgments.
It can automatically evaluate—and even help improve—the consistency of modern generative chatbots.

Why it matters: More consistent assistants feel smarter, safer, and more trustworthy.

Paper: http://arxiv.org/abs/2012.13391v2
Authors: Yixin Nie, Mary Williamson, Mohit Bansal, Douwe Kiela, Jason Weston

Paper: http://arxiv.org/abs/2012.13391v2

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AI Chatbots NLP MachineLearning ConversationalAI Consistency Research

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