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Uncovering Work from Words: LLM-Based Information Extraction from Historical Petitions

We investigate the extraction and normalisation of phrases describing work from 18th-century Swedish petitions using four LLMs: GPT-4o, Llama-3 70B/8B, and Mixtral-8x7B. Performance is evaluated across four configurations: isolated extraction, isolated normalisation, a staged pipeline, and a combined multitasking setup, using both full and filtered texts (with formal greetings and closing sections removed). While exact phrase matching remains low (F1 < .10), token-level and semantic similarity scores suggest that models consistently locate relevant topical regions. Semantic similarity scores must however be interpreted with caution, since they are often only marginally higher than an average baseline. Results reveal a "multitasking paradox": combined extraction and normalisation improves phrase location for high-parameter models but degrades normalisation precision. Furthermore, normalisation benefits from the context of a staged pipeline compared to isolated tasks, while text filtering has only marginal effects. Despite a tendency towards over-prediction, qualitative analysis suggests that models can detect plausible work-related expressions missed by human annotators. These findings illustrate the challenges of historical extraction and suggest that hybrid human–machine workflows are a promising approach for enhancing coverage and interpretability in cultural heritage research.