Summary of Bayesjudge: Bayesian Kernel Language Modelling with Confidence Uncertainty in Legal Judgment Prediction, by Ubaid Azam et al.
BayesJudge: Bayesian Kernel Language Modelling with Confidence Uncertainty in Legal Judgment Prediction
by Ubaid Azam, Imran Razzak, Shelly Vishwakarma, Hakim Hacid, Dell Zhang, Shoaib Jameel
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel Bayesian approach called BayesJudge, which leverages transformer-based deep neural networks (DNNs) like BERT to predict legal judgments with reliable confidence. The method uses Bayesian kernel Monte Carlo dropout to quantify uncertainty through the synergy between DNNs and deep Gaussian Processes. This is achieved by leveraging informative priors and flexible data modelling via kernels, which surpasses existing methods in both predictive accuracy and confidence estimation as indicated by the brier score. The paper evaluates the model’s performance across diverse tasks using public legal datasets and introduces an optimal solution to automate the scrutiny of unreliable predictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The BayesJudge approach predicts legal judgments with reliable confidence, which is crucial for responsible AI applications in law. By combining transformer-based DNNs like BERT with Bayesian kernel Monte Carlo dropout, this method accurately assesses prediction uncertainty through informative priors and flexible data modelling. The result is superior predictive accuracy and confidence estimation compared to existing methods. |
Keywords
» Artificial intelligence » Bert » Dropout » Transformer
