Common errors in Generative AI systems used for knowledge extraction in the climate action domain

<ns3:p>Large Language Models (LLMs) and, more specifically, the Generative Pre-Trained Transformers (GPT) can help stakeholders in climate action to explore digital knowledge bases, extract and utilize climate action knowledge in sustainable manner. However, LLMs are "probabilistic models of knowledge bases" that excel at generating convincing texts but cannot be entirely relied upon due to the probabilistic nature of the information produced. This brief report illustrates the problem space by shedding some light on the issues of incomplete answers, hallucinations, and misinformation threats.</ns3:p>

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Publication Details

Journal
Open Research Europe
Published
2024-10-16
DOI
https://doi.org/10.12688/openreseurope.17258.1
Citations
2
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
0.69

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article

Common errors in Generative AI systems used for knowledge extraction in the climate action domain

Marcelo Pias, Denis Havlik
2 citations
Open Research Europe
Topic Modeling
0.69
article

Common errors in Generative AI systems used for knowledge extraction in the climate action domain

Marcelo Pias, Denis Havlik
article en
2 citations

Abstract

<ns3:p>Large Language Models (LLMs) and, more specifically, the Generative Pre-Trained Transformers (GPT) can help stakeholders in climate action to explore digital knowledge bases, extract and utilize climate action knowledge in sustainable manner. However, LLMs are "probabilistic models of knowledge bases" that excel at generating convincing texts but cannot be entirely relied upon due to the probabilistic nature of the information produced. This brief report illustrates the problem space by shedding some light on the issues of incomplete answers, hallucinations, and misinformation threats.</ns3:p>

Open Research EuropeVol. 4
HORIZON EUROPE Framework Programme
Climate action
Openalex Percentile: Top 24%
Topic Modeling
0.69
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