# Measuring the `I don't know' Problem through the Lens of Gricean Quantity

arXiv: https://arxiv.org/abs/2010.12786 · HTML: https://mlsanity.com/paper/2010.12786/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2020.

## Built on by (most cited first, 1 of 1)

- [Using Natural Language Inference to Improve Persona Extraction from Dialogue in a New Domain](https://mlsanity.com/paper/2401.06742/) (2024)

## Abstract (arXiv)

We consider the intrinsic evaluation of neural generative dialog models through the lens of Grice's Maxims of Conversation (1975). Based on the maxim of Quantity (be informative), we propose Relative Utterance Quantity (RUQ) to diagnose the `I don't know' problem, in which a dialog system produces generic responses. The linguistically motivated RUQ diagnostic compares the model score of a generic response to that of the reference response. We find that for reasonable baseline models, `I don't know' is preferred over the reference the majority of the time, but this can be reduced to less than 5% with hyperparameter tuning. RUQ allows for the direct analysis of the `I don't know' problem, which has been addressed but not analyzed by prior work.