LaMDA: Language Models for Dialog Applications

We present LaMDA: Language Models for Dialog Applications. We quantify factuality using a groundedness metric, and we find that our approach enables the model to generate responses grounded in known sources, rather than responses that merely sound plausible.

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• 1 LaMDA is a family of Transformer-based neural language models specialized for dialog, which have up to 137B parameters and are pre-trained on 1.56T words of public dialog data and web text.
• 2 While model scaling alone can improve quality, it shows less improvements on safety and factual grounding.
• 3 We demonstrate that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to significant improvements towards the two key challenges of safety and factual grounding.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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