Common errors in Generative AI systems used for knowledge extraction in the climate action domain
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914663606779904 |
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| author | Havlik, Denis Pias, Marcelo |
| author_facet | Havlik, Denis Pias, Marcelo |
| contents | Large Language Models (LLMs) and, more specifically, the Generative Pre-Trained Transformers (GPT) can help stakeholders in climate action explore digital knowledge bases and extract and utilize climate action knowledge in a 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 with examples of LLM responses to some of the questions of relevance to climate action. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_00830 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Common errors in Generative AI systems used for knowledge extraction in the climate action domain Havlik, Denis Pias, Marcelo Computers and Society Large Language Models (LLMs) and, more specifically, the Generative Pre-Trained Transformers (GPT) can help stakeholders in climate action explore digital knowledge bases and extract and utilize climate action knowledge in a 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 with examples of LLM responses to some of the questions of relevance to climate action. |
| title | Common errors in Generative AI systems used for knowledge extraction in the climate action domain |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2402.00830 |