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From AIR to (AI)R : The use of LLM for interpreting archaeological excavation data

Nurra, Federico ; Dell'Unto, Nicolo LU orcid and Derudas, Paola LU orcid (2024) Thematic Semester Digital Humanities and Artificial intelligence.
Abstract
Since 2021, the DarkLab at Lund University (LU) and the Digital Research Service at the French National Institute of Art History (INHA) have collaborated to develop state-of-the-art digital systems and tools for the management and publication of archaeological data, including information from fieldwork and artefact collections. Drawing on the combined expertise of both institutions, this partnership has led to the successful creation and launch of AIR (Archaeological Interactive Report). Following the international workshop on ‘Advanced 3D Archaeological Documentation and Linked Open Data’, held in Lund, Sweden, 17-19 April 2024, we began testing large language models (LLMs) for processing, transforming and interpreting archaeological... (More)
Since 2021, the DarkLab at Lund University (LU) and the Digital Research Service at the French National Institute of Art History (INHA) have collaborated to develop state-of-the-art digital systems and tools for the management and publication of archaeological data, including information from fieldwork and artefact collections. Drawing on the combined expertise of both institutions, this partnership has led to the successful creation and launch of AIR (Archaeological Interactive Report). Following the international workshop on ‘Advanced 3D Archaeological Documentation and Linked Open Data’, held in Lund, Sweden, 17-19 April 2024, we began testing large language models (LLMs) for processing, transforming and interpreting archaeological data, with very promising results. The source data, accessible via the AIR API, is structured in JSON-LD and formalized according to the most widely used ontologies in the field, such as CIDOC CRM and CRM-Archaeo. We have tested two prominent LLMs, GPT-4 by OpenAI and the Mistral Large model by Mistral AI, to analyze this data. In this talk, we will present the results of this experiment: we will focus on data structure, standardized models, and the nuanced challenges of integrating semantics and ontologies into archaeological descriptions and narratives. The presentation will illustrate our approach to improving the interpretation of archaeological data using Large Language Models. (Less)
Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Contribution to conference
publication status
published
subject
keywords
Digital archaeology, LLMs, open data, archaeological interpretations
conference name
Thematic Semester Digital Humanities and Artificial intelligence.
conference location
Paris, France
conference dates
2024-12-10 - 2024-12-12
language
English
LU publication?
yes
id
0712fd28-19bf-42e6-98b5-15bda5c56524
alternative location
https://semtemiahn.hypotheses.org/final-conference
date added to LUP
2024-12-13 09:31:51
date last changed
2025-04-04 14:19:15
@misc{0712fd28-19bf-42e6-98b5-15bda5c56524,
  abstract     = {{Since 2021, the DarkLab at Lund University (LU) and the Digital Research Service at the French National Institute of Art History (INHA) have collaborated to develop state-of-the-art digital systems and tools for the management and publication of archaeological data, including information from fieldwork and artefact collections. Drawing on the combined expertise of both institutions, this partnership has led to the successful creation and launch of AIR (Archaeological Interactive Report). Following the international workshop on ‘Advanced 3D Archaeological Documentation and Linked Open Data’, held in Lund, Sweden, 17-19 April 2024, we began testing large language models (LLMs) for processing, transforming and interpreting archaeological data, with very promising results. The source data, accessible via the AIR API, is structured in JSON-LD and formalized according to the most widely used ontologies in the field, such as CIDOC CRM and CRM-Archaeo. We have tested two prominent LLMs, GPT-4 by OpenAI and the Mistral Large model by Mistral AI, to analyze this data. In this talk, we will present the results of this experiment: we will focus on data structure, standardized models, and the nuanced challenges of integrating semantics and ontologies into archaeological descriptions and narratives. The presentation will illustrate our approach to improving the interpretation of archaeological data using Large Language Models.}},
  author       = {{Nurra, Federico and Dell'Unto, Nicolo and Derudas, Paola}},
  keywords     = {{Digital archaeology; LLMs; open data; archaeological interpretations}},
  language     = {{eng}},
  title        = {{From AIR to (AI)R : The use of LLM for interpreting archaeological excavation data}},
  url          = {{https://semtemiahn.hypotheses.org/final-conference}},
  year         = {{2024}},
}