Search Results for author:
Found 10 papers, 4 papers with code
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.
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.
Exploring Nature: Datasets and Models for Analyzing Nature-Related Disclosures
Our approach is the first to respond to calls to assess corporate nature communication on a large scale.
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.
When Does Aggregating Multiple Skills with Multi-Task Learning Work? A Case Study in Financial NLP
Due to the task difficulty and data scarcity in the Financial NLP domain, we explore when aggregating such diverse skills from multiple datasets with MTL can work.
chatClimate: Grounding Conversational AI in Climate Science
The answers and their sources were evaluated by our team of IPCC authors, who used their expert knowledge to score the accuracy of the answers from 1 (very-low) to 5 (very-high).
Original or Translated? A Causal Analysis of the Impact of Translationese on Machine Translation Performance
We show that these two factors have a large causal effect on the MT performance, in addition to the test-model direction mismatch highlighted by existing work on the impact of translationese.
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP
The principle of independent causal mechanisms (ICM) states that generative processes of real world data consist of independent modules which do not influence or inform each other.
