Papers with Monte Carlo dropout
Page Stream Segmentation with LLMs: Challenges and Applications in Insurance Document Automation (2025.coling-industry)
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| Challenge: | Existing models for page Stream Segmentation (PSS) are inadequate for high-stakes applications. |
| Approach: | They propose to use large language models for page Stream Segmentation to apply parameter-efficient fine-tuning to real-world insurance data. |
| Outcome: | The proposed model outperforms baseline models in page- and stream-level segmentation accuracy. |
How Certain is Your Transformer? (2021.eacl-main)
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Artem Shelmanov, Evgenii Tsymbalov, Dmitri Puzyrev, Kirill Fedyanin, Alexander Panchenko, Maxim Panov
| Challenge: | Obtaining reliable uncertainty estimations for such neural networks (NNs) is challenging due to the huge number of parameters in these deep learning models. |
| Approach: | They propose to use Monte Carlo dropout to estimate uncertainty for Transformer-based models and construct inexpensive estimates using Determinantal Point Processes. |
| Outcome: | The proposed estimates improve the quality of detection of error-prone instances. |
Should We Trust This Summary? Bayesian Abstractive Summarization to The Rescue (2022.findings-acl)
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| Challenge: | Xu et al., 2019; Lewis e t al, 2019) show that Bayesian summarization methods can generate high quality summaries but suffer from a couple of issues when inputs lie far from the training data distribution. |
| Approach: | They propose to extend state-of-the-art summarization models with Monte Carlo dropout and perform multiple stochastic forward passes to approximate Bayesian inference. |
| Outcome: | The proposed method outperforms deterministic summarization models on multiple benchmark datasets. |
Uncertainty-Aware Machine Translation Evaluation (2021.findings-emnlp)
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| Challenge: | Several neural-based metrics have been proposed to evaluate machine translation quality, but they are trained on noisy, biased and scarce human judgements. |
| Approach: | They propose a method to evaluate machine translation quality using point estimates . they combine COMET framework with Monte Carlo dropout and deep ensembles . |
| Outcome: | The proposed methods perform well across multiple language pairs and with references. |
Disentangling Uncertainty in Machine Translation Evaluation (2022.emnlp-main)
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| Challenge: | Trainable evaluation metrics for machine translation (MT) exhibit strong correlation with human judgements, but they are often hard to interpret and might produce unreliable scores under noisy or out-of-domain data. |
| Approach: | They propose to use Monte Carlo dropout and deep ensembles to quantify uncertainty in machine translation and assess their ability to target different sources of aleatoric and epistemic uncertainty. |
| Outcome: | The proposed measures can target different sources of aleatoric and epistemic uncertainty, with a reduction in computational costs. |
Uncertainty-Aware Unlikelihood Learning Improves Generative Aspect Sentiment Quad Prediction (2023.findings-acl)
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| Challenge: | Existing studies focus on what to generate but ignore what not to generate . a template-agnostic method boosts original learning and reduces mistakes simultaneously . |
| Approach: | They propose a template-agnostic method to control the token-level generation . they introduce Monte Carlo dropout to understand the built-in uncertainty of pre-trained language models . |
| Outcome: | The proposed method boosts original learning and reduces mistakes simultaneously on four public datasets. |