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Found 22 papers, 12 papers with code
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
LegalBench: Prototyping a Collaborative Benchmark for Legal Reasoning
Finally-inspired by the Open Science movement-we make a call for the legal and computer science communities to join our efforts by contributing new tasks.
LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models
The advent of large language models (LLMs) and their adoption by the legal community has given rise to the question: what types of legal reasoning can LLMs perform?
Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset
One concern with the rise of large language models lies with their potential for significant harm, particularly from pretraining on biased, obscene, copyrighted, and private information.
When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset
While a Transformer architecture (BERT) pretrained on a general corpus (Google Books and Wikipedia) improves performance, domain pretraining (using corpus of approximately 3. 5M decisions across all courts in the U. S. that is larger than BERT's) with a custom legal vocabulary exhibits the most substantial performance gains with CaseHOLD (gain of 7. 2% on F1, representing a 12% improvement on BERT) and consistent performance gains across two other legal tasks.
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Temporal Cluster Matching for Change Detection of Structures from Satellite Imagery
For instance, in rural settings, the pre-construction area may look similar to the surrounding environment until the building is constructed.
Context-Aware Legal Citation Recommendation using Deep Learning
Lawyers and judges spend a large amount of time researching the proper legal authority to cite while drafting decisions.
FLawN-T5: An Empirical Examination of Effective Instruction-Tuning Data Mixtures for Legal Reasoning
In this work, we curate LawInstruct, a large legal instruction dataset, covering 17 jurisdictions, 24 languages and a total of 12M examples.
Enhancing Environmental Enforcement with Near Real-Time Monitoring: Likelihood-Based Detection of Structural Expansion of Intensive Livestock Farms
Much environmental enforcement in the United States has historically relied on either self-reported data or physical, resource-intensive, infrequent inspections.
Detecting Environmental Violations with Satellite Imagery in Near Real Time: Land Application under the Clean Water Act
We show that this system effectively appears to detect land application (PR AUC = 0. 93) and we uncover several outlier facilities which appear to apply regularly and excessively.
Affirmative Algorithms: The Legal Grounds for Fairness as Awareness
While there has been a flurry of research in algorithmic fairness, what is less recognized is that modern antidiscrimination law may prohibit the adoption of such techniques.
Reconciling Risk Allocation and Prevalence Estimation in Public Health Using Batched Bandits
In many public health settings, there is a perceived tension between allocating resources to known vulnerable areas and learning about the overall prevalence of the problem.
Integrating Reward Maximization and Population Estimation: Sequential Decision-Making for Internal Revenue Service Audit Selection
This approach has the potential to improve audit efficacy, while maintaining policy-relevant estimates of the tax gap.
Algorithmic Fairness and Vertical Equity: Income Fairness with IRS Tax Audit Models
First, we show how the use of more flexible machine learning (classification) methods -- as opposed to simpler models -- shifts audit burdens from high to middle-income taxpayers.
Entropy Regularization for Population Estimation
Entropy regularization is known to improve exploration in sequential decision-making problems.
MultiLegalPile: A 689GB Multilingual Legal Corpus
Large, high-quality datasets are crucial for training Large Language Models (LLMs).
SCALE: Scaling up the Complexity for Advanced Language Model Evaluation
In this paper, we introduce a novel NLP benchmark that poses challenges to current LLMs across four key dimensions: processing long documents (up to 50K tokens), utilizing domain specific knowledge (embodied in legal texts), multilingual understanding (covering five languages), and multitasking (comprising legal document to document Information Retrieval, Court View Generation, Leading Decision Summarization, Citation Extraction, and eight challenging Text Classification tasks).
Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools
While algorithmic fairness is a thriving area of research, in practice, mitigating issues of bias often gets reduced to enforcing an arbitrarily chosen fairness metric, either by enforcing fairness constraints during the optimization step, post-processing model outputs, or by manipulating the training data.
Estimating and Implementing Conventional Fairness Metrics With Probabilistic Protected Features
Unlike similar existing approaches, our methods take advantage of contextual information -- specifically, the relationships between a model's predictions and the probabilistic prediction of protected attributes, given the true protected attribute, and vice versa -- to provide tighter bounds on the true disparity.
Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models
Large language models (LLMs) have the potential to transform the practice of law, but this potential is threatened by the presence of legal hallucinations -- responses from these models that are not consistent with legal facts.
How well do LLMs cite relevant medical references? An evaluation framework and analyses
In this paper, we ask: do the sources that LLMs generate actually support the claims that they make?
On the Societal Impact of Open Foundation Models
To understand their risks of misuse, we design a risk assessment framework for analyzing their marginal risk.
