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    <abstract>The restoration and interpretation of fragmentary inscriptions remain central challenges in epigraphy, where scholars must reconstruct missing text and determine an inscription’s provenance and chronology from limited evidence. Ithaca, a neural model introduced in 2022, represented a landmark advance in this field, achieving highly accurate results in text restoration and spatio-temporal attribution. Since then, general-purpose large language models (LLMs) such as GPT, Claude, and Gemini have achieved remarkable versatility across many domains, raising the question of whether specialized architectures like Ithaca are still required. In this paper, we revisit Ithaca with a dual focus. First, we benchmark its performance against GPT-5, finding that Ithaca continues to substantially outperform a state-of-the-art general-purpose LLM used in a retrieval-augmented in-context learning setting. Second, we conduct a systematic analysis to characterize Ithaca’s behavior under varying conditions, including lacuna size and position, inscription origin, and semantic topic. Statistical analyses highlight its systematic strengths and weaknesses. Taken together, our results map Ithaca’s performance profile, enabling more informed use in research and teaching.</abstract>
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%0 Conference Proceedings
%T Ithaca Revisited: Benchmarking a Domain-Specific Model for Epigraphy in the Age of LLMs
%A Locaputo, Alessandro
%A Brunello, Andrea
%A Saccomanno, Nicola
%A Platanou, Paraskevi
%A Serra, Giuseppe
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F locaputo-etal-2026-ithaca
%X The restoration and interpretation of fragmentary inscriptions remain central challenges in epigraphy, where scholars must reconstruct missing text and determine an inscription’s provenance and chronology from limited evidence. Ithaca, a neural model introduced in 2022, represented a landmark advance in this field, achieving highly accurate results in text restoration and spatio-temporal attribution. Since then, general-purpose large language models (LLMs) such as GPT, Claude, and Gemini have achieved remarkable versatility across many domains, raising the question of whether specialized architectures like Ithaca are still required. In this paper, we revisit Ithaca with a dual focus. First, we benchmark its performance against GPT-5, finding that Ithaca continues to substantially outperform a state-of-the-art general-purpose LLM used in a retrieval-augmented in-context learning setting. Second, we conduct a systematic analysis to characterize Ithaca’s behavior under varying conditions, including lacuna size and position, inscription origin, and semantic topic. Statistical analyses highlight its systematic strengths and weaknesses. Taken together, our results map Ithaca’s performance profile, enabling more informed use in research and teaching.
%R 10.63317/3gucgvmwsf45
%U https://aclanthology.org/2026.lrec-1.82/
%U https://doi.org/10.63317/3gucgvmwsf45
%P 1054-1070

Markdown (Informal)

[Ithaca Revisited: Benchmarking a Domain-Specific Model for Epigraphy in the Age of LLMs](https://aclanthology.org/2026.lrec-1.82/) (Locaputo et al., LREC 2026)

• Ithaca Revisited: Benchmarking a Domain-Specific Model for Epigraphy in the Age of LLMs (Locaputo et al., LREC 2026)

ACL

• Alessandro Locaputo, Andrea Brunello, Nicola Saccomanno, Paraskevi Platanou, and Giuseppe Serra. 2026. Ithaca Revisited: Benchmarking a Domain-Specific Model for Epigraphy in the Age of LLMs. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 1054–1070, Palma de Mallorca, Spain. ELRA Language Resource Association.