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Found 21 papers, 16 papers with code
Machine Extraction of Tax Laws from Legislative Texts
Using a corpus of compiled codes from U. S. states containing labeled tax law sections, we train text classifiers to automatically tag tax-law documents and, further, to identify the associated revenue source (e. g. income, property, or sales).
Aligning Large Language Models with Diverse Political Viewpoints
Models aligned with this data can generate more accurate political viewpoints from Swiss parties, compared to commercial models such as ChatGPT.
DIRAS: Efficient LLM Annotation of Document Relevance in Retrieval Augmented Generation
Retrieval Augmented Generation (RAG) is widely employed to ground responses to queries on domain-specific documents.
Whose Preferences? Differences in Fairness Preferences and Their Impact on the Fairness of AI Utilizing Human Feedback
There is a growing body of work on learning from human feedback to align various aspects of machine learning systems with human values and preferences.
AFaCTA: Assisting the Annotation of Factual Claim Detection with Reliable LLM Annotators
With the rise of generative AI, automated fact-checking methods to combat misinformation are becoming more and more important.
Towards Faithful and Robust LLM Specialists for Evidence-Based Question-Answering
One avenue in reaching this goal is basing the answers on reliable sources.
Where Do People Tell Stories Online? Story Detection Across Online Communities
Story detection in online communities is a challenging task as stories are scattered across communities and interwoven with non-storytelling spans within a single text.
LePaRD: A Large-Scale Dataset of Judges Citing Precedents
We present the Legal Passage Retrieval Dataset LePaRD.
Translating Legalese: Enhancing Public Understanding of Court Opinions with Legal Summarizers
Judicial opinions are written to be persuasive and could build public trust in court decisions, yet they can be difficult for non-experts to understand.
The Law and NLP: Bridging Disciplinary Disconnects
Legal practice is intrinsically rooted in the fabric of language, yet legal practitioners and scholars have been slow to adopt tools from natural language processing (NLP).
Uncovering and Categorizing Social Biases in Text-to-SQL
In this work, we aim to uncover and categorize social biases in Text-to-SQL models.
Revisiting Automated Topic Model Evaluation with Large Language Models
Topic models are used to make sense of large text collections.
Legal Extractive Summarization of U.S. Court Opinions
This paper tackles the task of legal extractive summarization using a dataset of 430K U. S. court opinions with key passages annotated.
Human-Guided Fair Classification for Natural Language Processing
While existing research has started to address this gap, current methods are based on hardcoded word replacements, resulting in specifications with limited expressivity or ones that fail to fully align with human intuition (e. g., in cases of asymmetric counterfactuals).
Heroes, Villains, and Victims, and GPT-3: Automated Extraction of Character Roles Without Training Data
This paper shows how to use large-scale pre-trained language models to extract character roles from narrative texts without training data.
Media Slant is Contagious
This paper examines the diffusion of media slant, specifically how partisan content from national cable news affects local newspapers in the U. S., 2005-2008.
The Choice of Knowledge Base in Automated Claim Checking
Automated claim checking is the task of determining the veracity of a claim given evidence found in a knowledge base of trustworthy facts.
RELATIO: Text Semantics Capture Political and Economic Narratives
Social scientists have become increasingly interested in how narratives -- the stories in fiction, politics, and life -- shape beliefs, behavior, and government policies.
MemSum: Extractive Summarization of Long Documents Using Multi-Step Episodic Markov Decision Processes
We introduce MemSum (Multi-step Episodic Markov decision process extractive SUMmarizer), a reinforcement-learning-based extractive summarizer enriched at each step with information on the current extraction history.
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DocSCAN: Unsupervised Text Classification via Learning from Neighbors
We introduce DocSCAN, a completely unsupervised text classification approach using Semantic Clustering by Adopting Nearest-Neighbors (SCAN).
Evaluating Document Representations for Content-based Legal Literature Recommendations
Simultaneously, legal recommender systems are typically evaluated in small-scale user study without any public available benchmark datasets.
