# "None of the Above":Measure Uncertainty in Dialog Response Retrieval

arXiv: https://arxiv.org/abs/2004.01926 · HTML: https://mlsanity.com/paper/2004.01926/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2020.

## Built on by (most cited first, 8 of 8)

- [Uncertainty in Natural Language Processing: Sources, Quantification, and Applications](https://mlsanity.com/paper/2306.04459/) (2023)
- [InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning](https://mlsanity.com/paper/2205.12673/) (2022)
- [Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models](https://mlsanity.com/paper/2205.00176/) (2022)
- [On the Calibration and Uncertainty of Neural Learning to Rank Models](https://mlsanity.com/paper/2101.04356/) (2021)
- [Can Tool-augmented Large Language Models be Aware of Incomplete Conditions?](https://mlsanity.com/paper/2406.12307/) (2024)
- [Benchmarking candidate coverage and rejection policy transfer in typed decision models](https://mlsanity.com/paper/2610.03387/) (2026)
- [Evaluating Predictive Uncertainty under Distributional Shift on Dialogue Dataset](https://mlsanity.com/paper/2109.00186/) (2021)
- [On the Calibration and Uncertainty with P'olya-Gamma Augmentation for Dialog Retrieval Models](https://mlsanity.com/paper/2303.08606/) (2023)

## Abstract (arXiv)

This paper discusses the importance of uncovering uncertainty in end-to-end dialog tasks, and presents our experimental results on uncertainty classification on the Ubuntu Dialog Corpus. We show that, instead of retraining models for this specific purpose, the original retrieval model's underlying confidence concerning the best prediction can be captured with trivial additional computation.