mlsanityData to 14 Oct 2026

The Effects of Data Augmentation on Confidence Estimation for LLMs

arXiv 2506.11046 ↗First posted 21 May 2025cs.LGcited by 0

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Abstract (arXiv)

Confidence estimation is crucial for reflecting the reliability of large language models (LLMs), particularly in the widely used closed-source models. Utilizing data augmentation for confidence estimation is viable, but discussions focus on specific augmentation techniques, limiting its potential. We study the impact of different data augmentation methods on confidence estimation. Our findings indicate that data augmentation strategies can achieve better performance and mitigate the impact of overconfidence. We investigate the influential factors related to this and discover that, while preserving semantic information, greater data diversity enhances the effectiveness of augmentation. Furthermore, the impact of different augmentation strategies varies across different range of application. Considering parameter transferability and usability, the random combination of augmentations is a promising choice.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 29 references that are in the map. First posted on arXiv 21 May 2025.

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References · 21

  1. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
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  11. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 788
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9 more
  1. 2021Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategiescited by 743
  2. 2021A Survey of Data Augmentation Approaches for NLPcited by 409
  3. 2022Prompting GPT-3 To Be Reliablecited by 260
  4. 2023SAC3: Reliable Hallucination Detection in Black-Box Language Models via Semantic-aware Cross-check Consistencycited by 90
  5. 2024Calibrating Language Models with Adaptive Temperature Scalingcited by 58
  6. 2024Confidence Under the Hood: An Investigation into the Confidence-Probability Alignment in Large Language Modelscited by 34
  7. 2021Text AutoAugment: Learning Compositional Augmentation Policy for Text Classificationcited by 15
  8. 2024Enhancing Language Model Factuality via Activation-Based Confidence Calibration and Guided Decodingcited by 14
  9. 2024Are Data Augmentation Methods in Named Entity Recognition Applicable for Uncertainty Estimation?cited by 3

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