32 citations · 118 across the 12 of their papers we have counts for
14 papers
LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models
Neel Guha, Julian Nyarko, Daniel E. Ho +37
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? To enable great…
ARB: Advanced Reasoning Benchmark for Large Language Models
Tomohiro Sawada, Daniel Paleka, Alexander Havrilla +6
Large Language Models (LLMs) have demonstrated remarkable performance on various quantitative reasoning and knowledge benchmarks. However, many of these benchmarks are losing utili…
Large Language Models as Tax Attorneys: A Case Study in Legal Capabilities Emergence
John J. Nay, David Karamardian, Sarah B. Lawsky +6
Better understanding of Large Language Models' (LLMs) legal analysis abilities can contribute to improving the efficiency of legal services, governing artificial intelligence, and…
Large Language Models as Fiduciaries: A Case Study Toward Robustly Communicating With Artificial Intelligence Through Legal Standards
John J. Nay
Artificial Intelligence (AI) is taking on increasingly autonomous roles, e.g., browsing the web as a research assistant and managing money. But specifying goals and restrictions fo…
Large Language Models as Corporate Lobbyists
John J. Nay
We demonstrate a proof-of-concept of a large language model conducting corporate lobbying related activities. An autoregressive large language model (OpenAI's text-davinci-003) det…
Law Informs Code: A Legal Informatics Approach to Aligning Artificial Intelligence with Humans
John J. Nay
We are currently unable to specify human goals and societal values in a way that reliably directs AI behavior. Law-making and legal interpretation form a computational engine that…
Climate-Contingent Finance
John Nay
Climate adaptation could yield significant benefits. However, the uncertainty of which future climate scenarios will occur decreases the feasibility of proactively adapting. Climat…
Aligning Artificial Intelligence with Humans through Public Policy
John Nay, James Daily
Given that Artificial Intelligence (AI) increasingly permeates our lives, it is critical that we systematically align AI objectives with the goals and values of humans. The human-A…
Gov2Vec: Learning Distributed Representations of Institutions and Their Legal Text
John J. Nay
We compare policy differences across institutions by embedding representations of the entire legal corpus of each institution and the vocabulary shared across all corpora into a co…
Predicting and Understanding Law-Making with Word Vectors and an Ensemble Model
John J. Nay
Out of nearly 70,000 bills introduced in the U.S. Congress from 2001 to 2015, only 2,513 were enacted. We developed a machine learning approach to forecasting the probability that…
Data-Driven Dynamic Decision Models
John J. Nay, Jonathan M. Gilligan
This article outlines a method for automatically generating models of dynamic decision-making that both have strong predictive power and are interpretable in human terms. This is u…
Betting and Belief: Prediction Markets and Attribution of Climate Change
John J. Nay, Martin Van der Linden, Jonathan M. Gilligan
Despite much scientific evidence, a large fraction of the American public doubts that greenhouse gases are causing global warming. We present a simulation model as a computational…
A Machine Learning Approach to Forecasting Remotely Sensed Vegetation Health
John J. Nay, Emily Burchfield, Jonathan Gilligan
Drought threatens food and water security around the world, and this threat is likely to become more severe under climate change. High resolution predictive information can help fa…
Predicting Human Cooperation
John J. Nay, Yevgeniy Vorobeychik
The Prisoner's Dilemma has been a subject of extensive research due to its importance in understanding the ever-present tension between individual self-interest and social benefit.…