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Found 9 papers, 6 papers with code
Improved Induction of Narrative Chains via Cross-Document Relations
The standard approach for inducing narrative chains considers statistics gathered per individual document.
LLMs Provide Unstable Answers to Legal Questions
An LLM is stable if it reaches the same conclusion when asked the identical question multiple times.
Gaps or Hallucinations? Gazing into Machine-Generated Legal Analysis for Fine-grained Text Evaluations
Large Language Models (LLMs) show promise as a writing aid for professionals performing legal analyses.
CLERC: A Dataset for Legal Case Retrieval and Retrieval-Augmented Analysis Generation
This dataset CLERC (Case Law Evaluation Retrieval Corpus), is constructed for training and evaluating models on their ability to (1) find corresponding citations for a given piece of legal analysis and to (2) compile the text of these citations (as well as previous context) into a cogent analysis that supports a reasoning goal.
BLT: Can Large Language Models Handle Basic Legal Text?
We find that the best publicly available LLMs like GPT-4 and Claude currently perform poorly on 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.
InteractiveIE: Towards Assessing the Strength of Human-AI Collaboration in Improving the Performance of Information Extraction
Learning template based information extraction from documents is a crucial yet difficult task.
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.
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.
