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Found 12 papers, 6 papers with code
Reframing Tax Law Entailment as Analogical Reasoning
We re-frame statutory reasoning as an analogy task, where each instance of the analogy task involves a combination of two instances of statutory reasoning.
BLT: Can Large Language Models Handle Basic Legal Text?
We find that the best publicly available LLMs like GPT-4, Claude, and {PaLM 2} currently perform poorly at basic legal text handling.
OpenAI Cribbed Our Tax Example, But Can GPT-4 Really Do Tax?
The authors explain where OpenAI got the tax law example in its livestream demonstration of GPT-4, why GPT-4 got the wrong answer, and how it fails to reliably calculate taxes.
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?
Can GPT-3 Perform Statutory Reasoning?
Statutory reasoning is the task of reasoning with facts and statutes, which are rules written in natural language by a legislature.
Asking the Right Questions in Low Resource Template Extraction
Information Extraction (IE) researchers are mapping tasks to Question Answering (QA) in order to leverage existing large QA resources, and thereby improve data efficiency.
Factoring Statutory Reasoning as Language Understanding Challenges
Statutory reasoning is the task of determining whether a legal statute, stated in natural language, applies to the text description of a case.
Human Schema Curation via Causal Association Rule Mining
Event schemas are structured knowledge sources defining typical real-world scenarios (e. g., going to an airport).
A Dataset for Statutory Reasoning in Tax Law Entailment and Question Answering
Legislation can be viewed as a body of prescriptive rules expressed in natural language.
Multiview Representation Learning for a Union of Subspaces
Canonical correlation analysis (CCA) is a popular technique for learning representations that are maximally correlated across multiple views in data.
Large-Scale, Diverse, Paraphrastic Bitexts via Sampling and Clustering
Producing diverse paraphrases of a sentence is a challenging task.
Learning from Multiview Correlations in Open-Domain Videos
This shows it is possible to learn reliable representations across disparate, unaligned and noisy modalities, and encourages using the proposed approach on larger datasets.
