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Found 18 papers, 9 papers with code
Evidence Selection as a Token-Level Prediction Task
In Automated Claim Verification, we retrieve evidence from a knowledge base to determine the veracity of a claim.
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.
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.
Automated Fact-Checking of Climate Change Claims with Large Language Models
This paper presents Climinator, a novel AI-based tool designed to automate the fact-checking of climate change claims.
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).
CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools
In the face of climate change, are companies really taking substantial steps toward more sustainable operations?
Paradigm Shift in Sustainability Disclosure Analysis: Empowering Stakeholders with CHATREPORT, a Language Model-Based Tool
This paper introduces a novel approach to enhance Large Language Models (LLMs) with expert knowledge to automate the analysis of corporate sustainability reports by benchmarking them against the Task Force for Climate-Related Financial Disclosures (TCFD) recommendations.
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.
Enhancing Large Language Models with Climate Resources
Large language models (LLMs) have significantly transformed the landscape of artificial intelligence by demonstrating their ability in generating human-like text across diverse topics.
Environmental Claim Detection
To transition to a green economy, environmental claims made by companies must be reliable, comparable, and verifiable.
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.
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.
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).
Team DOMLIN: Exploiting Evidence Enhancement for the FEVER Shared Task
This paper contains our system description for the second Fact Extraction and VERification (FEVER) challenge.
DOMLIN at SemEval-2019 Task 8: Automated Fact Checking exploiting Ratings in Community Question Answering Forums
Our hand-in for subtask A consists of a fine-tuned classifier from this BERT checkpoint.
