Éditorial
This article examines the potential and limitations of LLMs (Large Language Models) for analyzing narratives, focusing on corporate profits as a case study. Building on the “narrative turn” in social and human science (SHS) research, it highlights how AI (artificial intelligence) – based methods...
Enregistré dans:
| Auteurs principaux: | , |
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| Format: | Article ou chapitre numérique |
| Langue: | Français |
| Publié: |
2013
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| Accès en ligne: | Accès Université d'Orléans et IFPM Accès Université d'Orléans et IFPM |
| Résumé: | This article examines the potential and limitations of LLMs (Large Language Models) for analyzing narratives, focusing on corporate profits as a case study. Building on the “narrative turn” in social and human science (SHS) research, it highlights how AI (artificial intelligence) – based methods can scale up the identification, classification, and visualization of qualitative discourse across large corpora. The paper introduces the MILL-EHNAS project, which aims to develop annotated datasets, multimodal LLM evaluation frameworks, and new tools for short-form narrative detection to study how profit narratives influence accounting practices and fiscal policies. While emphasizing the promise of LLMs for integrating information-technology and SHS research, the article also discusses methodological risks, including overreliance on structured databases, potential loss of archival expertise, and new divisions of labor within academia. Overall, it argues that LLMs can transform narrative analysis but must be used critically, with attention to context, interpretability, and disciplinary balance. |
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