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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2508.15817 |
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| _version_ | 1866911116218597376 |
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| author | Simmering, Paul F. Schulz, Benedikt Tabino, Oliver Wittenburg, Georg |
| author_facet | Simmering, Paul F. Schulz, Benedikt Tabino, Oliver Wittenburg, Georg |
| contents | As organizations adopt retrieval-augmented generation (RAG) for their knowledge management systems (KMS), traditional market research deliverables face new functional demands. While PDF reports and slides have long served human readers, they are now also "read" by AI systems to answer user questions. To future-proof reports being delivered today, this study evaluates information loss during their ingestion into RAG systems. It compares how well PDF and PowerPoint (PPTX) documents converted to Markdown can be used by an LLM to answer factual questions in an end-to-end benchmark. Findings show that while text is reliably extracted, significant information is lost from complex objects like charts and diagrams. This suggests a need for specialized, AI-native deliverables to ensure research insights are not lost in translation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_15817 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Meet Your New Client: Writing Reports for AI -- Benchmarking Information Loss in Market Research Deliverables Simmering, Paul F. Schulz, Benedikt Tabino, Oliver Wittenburg, Georg Computation and Language Computers and Society As organizations adopt retrieval-augmented generation (RAG) for their knowledge management systems (KMS), traditional market research deliverables face new functional demands. While PDF reports and slides have long served human readers, they are now also "read" by AI systems to answer user questions. To future-proof reports being delivered today, this study evaluates information loss during their ingestion into RAG systems. It compares how well PDF and PowerPoint (PPTX) documents converted to Markdown can be used by an LLM to answer factual questions in an end-to-end benchmark. Findings show that while text is reliably extracted, significant information is lost from complex objects like charts and diagrams. This suggests a need for specialized, AI-native deliverables to ensure research insights are not lost in translation. |
| title | Meet Your New Client: Writing Reports for AI -- Benchmarking Information Loss in Market Research Deliverables |
| topic | Computation and Language Computers and Society |
| url | https://arxiv.org/abs/2508.15817 |