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Found 9 papers, 3 papers with code
ARB: Advanced Reasoning Benchmark for Large Language Models
As a subset of ARB, we introduce a challenging set of math and physics problems which require advanced symbolic reasoning and domain knowledge.
Large Language Models as Tax Attorneys: A Case Study in Legal Capabilities Emergence
Better understanding of Large Language Models' (LLMs) legal analysis abilities can contribute to improving the efficiency of legal services, governing artificial intelligence, and leveraging LLMs to identify inconsistencies in law.
Large Language Models as Fiduciaries: A Case Study Toward Robustly Communicating With Artificial Intelligence Through Legal Standards
Through an empirical study on thousands of evaluation labels we constructed from U. S. court opinions, we demonstrate that large language models (LLMs) are beginning to exhibit an "understanding" of one of the most relevant legal standards for AI agents: fiduciary obligations.
Large Language Models as Corporate Lobbyists
We use hundreds of novel ground-truth labels of the relevance of a bill to a company to benchmark the performance of the model.
Law Informs Code: A Legal Informatics Approach to Aligning Artificial Intelligence with Humans
We are currently unable to specify human goals and societal values in a way that reliably directs AI behavior.
Gov2Vec: Learning Distributed Representations of Institutions and Their Legal Text
We also learn representations for more fine-grained word sources: individual Presidents and (2-year) Congresses.
Predicting and Understanding Law-Making with Word Vectors and an Ensemble Model
To test the relative importance of text and context, we compared the text model to a context-only model that uses variables such as whether the bill's sponsor is in the majority party.
Betting and Belief: Prediction Markets and Attribution of Climate Change
We conduct sensitivity analyses to determine how a variety of factors describing both the market and the physical climate may affect traders' beliefs about the cause of global climate change.
Multiagent Systems Physics and Society Economics
Data-Driven Dynamic Decision Models
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.
