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Found 18 papers, 6 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.
ClimateBERT-NetZero: Detecting and Assessing Net Zero and Reduction Targets
Public and private actors struggle to assess the vast amounts of information about sustainability commitments made by various institutions.
Assessing Large Language Models on Climate Information
We evaluate several recent LLMs and conduct a comprehensive analysis of the results, shedding light on both the potential and the limitations of LLMs in the realm of climate communication.
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).
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.
Towards Climate Awareness in NLP Research
We argue that this deficiency is one of the reasons why very few publications in NLP report key figures that would allow a more thorough examination of environmental impact.
ClimateBert: A Pretrained Language Model for Climate-Related Text
Over the recent years, large pretrained language models (LM) have revolutionized the field of natural language processing (NLP).
ClimaText: A Dataset for Climate Change Topic Detection
Climate change communication in the mass media and other textual sources may affect and shape public perception.
CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims
We introduce CLIMATE-FEVER, a new publicly available dataset for verification of climate change-related claims.
Generating Fact Checking Summaries for Web Claims
SUMO further generates an extractive summary by presenting a diversified set of sentences from the documents that explain its decision on the correctness of the textual claim.
MuSeM: Detecting Incongruent News Headlines using Mutual Attentive Semantic Matching
On the other hand, more recent works that use headline guided attention to learn a headline derived contextual representation of the news body also result in convoluting overall representation due to the news body's lengthiness.
