8 citations · 34 across the 10 of their papers we have counts for
16 papers
Who Checks the Citations? Benchmarking Legal Hallucination Detection
Patty Liu, Dominik Stammbach, Peter Henderson
Attorneys, judges, and pro se filers increasingly use AI to draft legal documents, yet these tools frequently fabricate citations. Despite predictions that newer models would hallu…
Aligning Large Language Models with Diverse Political Viewpoints
Dominik Stammbach, Philine Widmer, Eunjung Cho +2
Large language models such as ChatGPT exhibit striking political biases. If users query them about political information, they often take a normative stance. To overcome this, we a…
AFaCTA: Assisting the Annotation of Factual Claim Detection with Reliable LLM Annotators
Jingwei Ni, Minjing Shi, Dominik Stammbach +3
With the rise of generative AI, automated fact-checking methods to combat misinformation are becoming more and more important. However, factual claim detection, the first step in a…
Automated Fact-Checking of Climate Change Claims with Large Language Models
Markus Leippold, Saeid Ashraf Vaghefi, Dominik Stammbach +10
This paper presents Climinator, a novel AI-based tool designed to automate the fact-checking of climate change claims. Utilizing an array of Large Language Models (LLMs) informed b…
LePaRD: A Large-Scale Dataset of Judges Citing Precedents
Robert Mahari, Dominik Stammbach, Elliott Ash +1
We present the Legal Passage Retrieval Dataset LePaRD. LePaRD is a massive collection of U.S. federal judicial citations to precedent in context. The dataset aims to facilitate wor…
Translating Legalese: Enhancing Public Understanding of Court Opinions with Legal Summarizers
Elliott Ash, Aniket Kesari, Suresh Naidu +2
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. We present a pipeline for u…
The Law and NLP: Bridging Disciplinary Disconnects
Robert Mahari, Dominik Stammbach, Elliott Ash +1
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). At the…
CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools
Jingwei Ni, Julia Bingler, Chiara Colesanti-Senni +10
In the face of climate change, are companies really taking substantial steps toward more sustainable operations? A comprehensive answer lies in the dense, information-rich landscap…
Paradigm Shift in Sustainability Disclosure Analysis: Empowering Stakeholders with CHATREPORT, a Language Model-Based Tool
Jingwei Ni, Julia Bingler, Chiara Colesanti-Senni +10
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 th…
Legal Extractive Summarization of U.S. Court Opinions
Emmanuel Bauer, Dominik Stammbach, Nianlong Gu +1
This paper tackles the task of legal extractive summarization using a dataset of 430K U.S. court opinions with key passages annotated. According to automated summary quality metric…
Revisiting Automated Topic Model Evaluation with Large Language Models
Dominik Stammbach, Vilém Zouhar, Alexander Hoyle +2
Topic models are used to make sense of large text collections. However, automatically evaluating topic model output and determining the optimal number of topics both have been long…
Enhancing Large Language Models with Climate Resources
Mathias Kraus, Julia Anna Bingler, Markus Leippold +5
Large language models (LLMs) have significantly transformed the landscape of artificial intelligence by demonstrating their ability in generating human-like text across diverse top…
Environmental Claim Detection
Dominik Stammbach, Nicolas Webersinke, Julia Anna Bingler +2
To transition to a green economy, environmental claims made by companies must be reliable, comparable, and verifiable. To analyze such claims at scale, automated methods are needed…
Heroes, Villains, and Victims, and GPT-3: Automated Extraction of Character Roles Without Training Data
Dominik Stammbach, Maria Antoniak, Elliott Ash
This paper shows how to use large-scale pre-trained language models to extract character roles from narrative texts without training data. Queried with a zero-shot question-answeri…
The Choice of Knowledge Base in Automated Claim Checking
Dominik Stammbach, Boya Zhang, Elliott Ash
Automated claim checking is the task of determining the veracity of a claim given evidence found in a knowledge base of trustworthy facts. While previous work has taken the knowled…
DocSCAN: Unsupervised Text Classification via Learning from Neighbors
Dominik Stammbach, Elliott Ash
We introduce DocSCAN, a completely unsupervised text classification approach using Semantic Clustering by Adopting Nearest-Neighbors (SCAN). For each document, we obtain semantical…