Discovering Explanatory Sentences in Legal Case Decisions Using Pre-trained Language Models (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Understanding written laws is difficult because the abstract rules must account for a variety of situations, even those not yet encountered. |
| Approach: | They constructed a dataset of 26,959 sentences and labeled them in terms of their usefulness for explaining selected legal concepts. |
| Outcome: | The proposed models outperform the prior approaches and can learn surprisingly sophisticated features. |
Similar Papers
Legal Judgment Reimagined: PredEx and the Rise of Intelligent AI Interpretation in Indian Courts (2024.findings-acl)
Copied to clipboard
| Challenge: | Prediction with Explanation is the largest expert-annotated dataset for legal judgment prediction and explanation in the Indian context . |
| Approach: | They propose to use an annotated legal judgment prediction corpus to improve models' accuracy . they employ transformer-based models tailored for both general and Indian legal contexts . |
| Outcome: | The proposed system improves the accuracy and explanatory depth of models for legal judgments. |
Text Classification and Prediction in the Legal Domain (2022.lrec-1)
Copied to clipboard
| Challenge: | a case study combines text classification and legal judgment prediction for flight compensation . a human-in-the-loop model outperformed human prediction when predicting a claim being successful . |
| Approach: | They combine transformer-based classification models with human-in-the-loop data to classify airlines' responses to flight compensation claims. |
| Outcome: | The proposed models outperform human prediction when predicting a legal claim success. |
What Context Features Can Transformer Language Models Use? (2021.acl-long)
Copied to clipboard
| Challenge: | Recent studies show that transformer-based language models benefit from conditioning on contexts of hundreds to thousands of previous tokens. |
| Approach: | They propose to use lexical and structural information to ablate usable information in transformer language models. |
| Outcome: | The proposed model improves when conditioning on contexts of thousands of previous tokens. |
Leveraging Large Language Models for Learning Complex Legal Concepts through Storytelling (2024.acl-long)
Copied to clipboard
Hang Jiang, Xiajie Zhang, Robert Mahari, Daniel Kessler, Eric Ma, Tal August, Irene Li, Alex Pentland, Yoon Kim, Deb Roy, Jad Kabbara
| Challenge: | a novel application of large language models (LLMs) to legal education helps non-experts learn complex legal concepts . authors find storytelling helps nonexperts understand complex legal terms and concepts compared to definitions . |
| Approach: | They propose a novel application of large language models to legal education . they use LLMs to generate legal stories explaining complex legal concepts . |
| Outcome: | The proposed method improves comprehension and interest among non-native speakers compared to definitions . the novel method also shows that non-experts retain more stories . |
Deconfounding Legal Judgment Prediction for European Court of Human Rights Cases Towards Better Alignment with Experts (2022.emnlp-main)
Copied to clipboard
| Challenge: | Legal Judgement Prediction systems without expert-informed adjustments can be vulnerable to shallow, distracting surface signals. |
| Approach: | They propose to use domain expertise to identify statistically predictive but legally irrelevant information and adopt adversarial training to prevent it from relying on it. |
| Outcome: | The proposed model aligns better with expert rationales than baseline models . the results are compared with an existing benchmark dataset of human rights cases . |
Automated Refugee Case Analysis: A NLP Pipeline for Supporting Legal Practitioners (2023.findings-acl)
Copied to clipboard
| Challenge: | In Canada, retrieving similar cases and their analysis is a key part of legal work . long processing times are due to a significant backlog and to the amount of work required from counsels . |
| Approach: | They propose to extend existing neural named-entity recognition models to retrieve 19 categories of items from refugee cases. |
| Outcome: | The proposed pipeline achieves a superior F1- score on five of the targeted categories and superior to 80% on an additional 4 categories. |
BriefMe: A Legal NLP Benchmark for Assisting with Legal Briefs (2025.findings-acl)
Copied to clipboard
| Challenge: | a core part of legal work that has been underexplored in Legal NLP is the writing and editing of legal briefs. |
| Approach: | They propose to use large language models to help legal professionals with writing briefs by capturing and evaluating their abilities in language models. |
| Outcome: | The proposed tasks show that the models perform well on arguments summarization, argument completion, and case retrieval tasks. |
LexGLUE: A Benchmark Dataset for Legal Language Understanding in English (2022.acl-long)
Copied to clipboard
Ilias Chalkidis, Abhik Jana, Dirk Hartung, Michael Bommarito, Ion Androutsopoulos, Daniel Katz, Nikolaos Aletras
| Challenge: | Laws and their interpretations, legal arguments and agreements are typically expressed in writing. |
| Approach: | They propose a benchmark to evaluate model performance across legal NLU tasks . they also evaluate several generic and legal-oriented models . |
| Outcome: | The proposed model performs better across multiple tasks than previous models. |
Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation (2022.aacl-main)
Copied to clipboard
Abhay Shukla, Paheli Bhattacharya, Soham Poddar, Rajdeep Mukherjee, Kripabandhu Ghosh, Pawan Goyal, Saptarshi Ghosh
| Challenge: | Summarization of legal case judgement documents is a challenging problem in Legal NLP. |
| Approach: | They propose to use extractive and abstractive summarization methods to evaluate legal document summarizing systems. |
| Outcome: | The proposed methods have been evaluated on three legal summarization datasets. |
CLEAR: A Framework Enabling Large Language Models to Discern Confusing Legal Paragraphs (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing work focuses on enabling LLMs to leverage legal rules to tackle complex legal reasoning tasks, but ignores their ability to understand legal rules. |
| Approach: | They propose a legal paragraph prediction task that aims to predict the legal paragraph given criminal facts and a framework CLEAR to enhance their legal reasoning ability. |
| Outcome: | The proposed model improves the ability of LLMs to analyze legal cases with the guidance of legal rule insights. |