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Found 8 papers, 2 papers with code
A Smart System to Generate and Validate Question Answer Pairs for COVID-19 Literature
Automatically generating question answer (QA) pairs from the rapidly growing coronavirus-related literature is of great value to the medical community.
Evaluating AI for Law: Bridging the Gap with Open-Source Solutions
This study evaluates the performance of general-purpose AI, like ChatGPT, in legal question-answering tasks, highlighting significant risks to legal professionals and clients.
Prototype-Based Interpretability for Legal Citation Prediction
Deep learning has made significant progress in the past decade, and demonstrates potential to solve problems with extensive social impact.
A Simple and Effective Framework for Strict Zero-Shot Hierarchical Classification
To address these limitations, we propose the use of entailment-contradiction prediction in conjunction with LLMs, which allows for strong performance in a strict zero-shot setting.
Towards Legally Enforceable Hate Speech Detection for Public Forums
With this task definition, automatic hate speech detection can be more closely aligned to enforceable laws, and hence assist in more rigorous enforcement of legal protections against harmful speech in public forums.
Prefix Propagation: Parameter-Efficient Tuning for Long Sequences
Parameter-efficient tuning aims to mitigate the large memory requirements of adapting pretrained language models for downstream tasks.
Parameter-Efficient Legal Domain Adaptation
Unfortunately, parameter-efficient methods perform poorly with small amounts of data, which are common in the legal domain (where data labelling costs are high).
Interpretable Low-Resource Legal Decision Making
In this work, we utilize deep learning models in the area of trademark law to shed light on the issue of likelihood of confusion between trademarks.
