# Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics

arXiv: https://arxiv.org/abs/2506.00637 · HTML: https://mlsanity.com/paper/2506.00637/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 35 references that are in the map.
- First posted on arXiv 31 May 2025.

## Builds on (most cited first, 21 of 21)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback](https://mlsanity.com/paper/2305.14975/) (2023)
- [Teaching Models to Express Their Uncertainty in Words](https://mlsanity.com/paper/2205.14334/) (2022)
- [Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness](https://mlsanity.com/paper/2006.10108/) (2020)
- [Selective Question Answering under Domain Shift](https://mlsanity.com/paper/2006.09462/) (2020)
- [Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities](https://mlsanity.com/paper/2405.20003/) (2024)
- [Ensemble Distribution Distillation](https://mlsanity.com/paper/1905.00076/) (2019)
- [Correcting Length Bias in Neural Machine Translation](https://mlsanity.com/paper/1808.10006/) (2018)
- [Abstractive Summarization of Reddit Posts with Multi-level Memory Networks](https://mlsanity.com/paper/1811.00783/) (2018)
- [Calibration of Encoder Decoder Models for Neural Machine Translation](https://mlsanity.com/paper/1903.00802/) (2019)
- [DebateSum: A large-scale argument mining and summarization dataset](https://mlsanity.com/paper/2011.07251/) (2020)
- [Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformers](https://mlsanity.com/paper/2006.08344/) (2020)
- [On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Study](https://mlsanity.com/paper/2304.08653/) (2023)
- [Enhancing Confidence Expression in Large Language Models Through Learning from Past Experience](https://mlsanity.com/paper/2404.10315/) (2024)
- [Combining Data Generation and Active Learning for Low-Resource Question Answering](https://mlsanity.com/paper/2211.14880/) (2022)
- [Logit-Based Ensemble Distribution Distillation for Robust Autoregressive Sequence Uncertainties](https://mlsanity.com/paper/2305.10384/) (2023)

## Built on by (most cited first, 4 of 4)

- [Sigmoid Head for Quality Estimation under Language Ambiguity](https://mlsanity.com/paper/2601.00680/) (2026)
- [Confident in a Confidence Score: Investigating the Sensitivity of Confidence Scores to Supervised Fine-Tuning](https://mlsanity.com/paper/2604.08974/) (2026)
- [Testing the Assumptions of Active Learning for Translation Tasks with Few Samples](https://mlsanity.com/paper/2604.08977/) (2026)
- [TRACE: Single-Pass Decoding-Trace Risk Localization for Generation Calibration](https://mlsanity.com/paper/2609.35387/) (2026)

## Abstract (arXiv)

Well-calibrated model confidence scores can improve the usefulness of text generation models. For example, users can be prompted to review predictions with low confidence scores, to prevent models from returning bad or potentially dangerous predictions. However, confidence metrics are not always well calibrated in text generation. One reason is that in generation, there can be many valid answers, which previous methods do not always account for. Hence, a confident model could distribute its output probability among multiple sequences because they are all valid. We propose task-agnostic confidence metrics suited to generation, which rely solely on the probabilities associated with the model outputs without the need for further fine-tuning or heuristics. Using these, we are able to improve the calibration of BART and Flan-T5 on summarization, translation, and QA datasets.