| _version_ | 1866902223852666880 |
|---|---|
| author | Havlik, Denis |
| author_facet | Havlik, Denis |
| contents | <p>This collection contains the results of four ClimEmpower / MAIA GenAI experiments. These experiments aim to asess how and to what extent the Generative AI models can help knowledge curators extract knowledge from documents they need to analyse. </p> <p>Concrete high-level research questions these experiments aim to resolve are:</p> <p><strong>RQ1: To what extent can the AI answers be used to formulate the final answers, without reading the whole document? </strong></p> <p><strong>RQ2: Which types of questions are easier or more difficult for GenAI models to answer?</strong></p> <p><strong>RQ3: How, and to what extent, can the answers be improved through prompt engineering? </strong></p> <p><strong>RQ4: To what extent do the GenAI models follow instructions to base the answers (only) on the content provided in the document?</strong></p> <p><strong>RQ5: How does the choice of GenAI model reflect in experiment results?</strong></p> <p>In addition, we were also interested in finding out the ways to further improve the SumQA, a Generative AI service that was developed in the MAIA project and supports batch-processing of documents.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15101319 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | GenAI experiments: Extracting knowledge from educational materials Havlik, Denis <p>This collection contains the results of four ClimEmpower / MAIA GenAI experiments. These experiments aim to asess how and to what extent the Generative AI models can help knowledge curators extract knowledge from documents they need to analyse. </p> <p>Concrete high-level research questions these experiments aim to resolve are:</p> <p><strong>RQ1: To what extent can the AI answers be used to formulate the final answers, without reading the whole document? </strong></p> <p><strong>RQ2: Which types of questions are easier or more difficult for GenAI models to answer?</strong></p> <p><strong>RQ3: How, and to what extent, can the answers be improved through prompt engineering? </strong></p> <p><strong>RQ4: To what extent do the GenAI models follow instructions to base the answers (only) on the content provided in the document?</strong></p> <p><strong>RQ5: How does the choice of GenAI model reflect in experiment results?</strong></p> <p>In addition, we were also interested in finding out the ways to further improve the SumQA, a Generative AI service that was developed in the MAIA project and supports batch-processing of documents.</p> |
| title | GenAI experiments: Extracting knowledge from educational materials |
| url | https://doi.org/10.5281/zenodo.15101319 |