This paper presents the results of the LegalLens Shared Task, focusing on detecting legal
violations within text in the wild across two sub-tasks: LegalLens-NER for identifying legal …

In recent years, the development and deployment of large language models (LLMs) have
revolutionized the field of natural language processing. However, these models are prone to …

In this work, we highlight and perform a comprehensive study on calibration attacks, a form
of adversarial attacks that aim to trap victim models to be heavily miscalibrated without …

Instruction tuning is an important step in making language models useful for direct user
interaction. However, many legal tasks remain out of reach for most open LLMs and there do …

In this work, we present two systems--Named Entity Resolution (NER) and Natural
Language Inference (NLI)--for detecting legal violations within unstructured textual data and …

We present Legal Argument Reasoning (LAR), a novel task designed to evaluate the legal
reasoning capabilities of Large Language Models (LLMs). The task requires selecting the …

Legal documents are often long and unstructured, making them challenging and time-
consuming to apprehend. An automatic system that can identify relevant entities and labels …

Large language models (LLMs) allow us to generate high-quality human-like text. One
interesting task in natural language processing (NLP) is named entity recognition (NER) …

This paper presents the methods used for LegalLens-2024 shared task, which focused on
detecting legal violations within unstructured textual data and associating these violations …

In this paper, we describe our system for LegalLens-2024 Shared Task on automatically
identifying legal violations from unstructured text sources. We participate in Subtask B …