Papers with COC
Through the Lens of Split Vote: Exploring Disagreement, Difficulty and Calibration in Legal Case Outcome Classification (2024.acl-long)
Copied to clipboard
| Challenge: | Existing methods for NLP calibration ignore inherent human label variation (HLV) split votes are a problem in high-stakes domains such as legal and medical decisions . |
| Approach: | They present a case outcome classification dataset with judges' vote distributions and build a taxonomy of disagreement with SV-specific subcategories. |
| Outcome: | The proposed model is compared against a judge vote distribution and assesses the alignment of perceived difficulty between models and humans. |
Rethinking-based Code Summarization with Chain of Comments (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods focus on learning a direct mapping from pure code to summaries, overlooking the heterogeneity gap between code and summary. |
| Approach: | They propose a framework that uses chain of comments as auxiliary intermediate information to bridge the gap between code and summaries. |
| Outcome: | The proposed framework outperforms baseline models and multiple code Large Language Models by a large margin. |
The Craft of Selective Prediction: Towards Reliable Case Outcome Classification - An Empirical Study on European Court of Human Rights Cases (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing COC tasks prioritize high task performance over model reliability . however, large models exhibit overconfidence and Monte Carlo dropout methods produce reliable confidence estimates . |
| Approach: | They conduct an empirical investigation into how various design choices affect the reliability of COC models within the framework of selective prediction. |
| Outcome: | The proposed model is able to predict the outcome of a legal case based on the text of the case facts and is compared with other models using a pre-training corpus. |
From Dissonance to Insights: Dissecting Disagreements in Rationale Construction for Case Outcome Classification (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing work in explainable COC has been limited to annotations by a single expert. |
| Approach: | They construct a two-level task-independent taxonomy from a dataset obtained from two experts in the domain of international human rights law . they find disagreements stem from underspecification of the legal context . |
| Outcome: | The proposed dataset is the first in legal NLP that focuses on human label variation. |