Small-Scale Testing on Generative AI and Post-OCR Correction in Historical Datasets Florentina Armaselu, University of Luxembourg florentina.armaselu@uni.lu Introduction Recent developments in large language models (LLMs) and generative AI (GenAI) chatbots such as Chat- GPT, Google Bard (now, Gemini) and YouChat (Brown et al., 2020; Manyika and Hsiao, 2023; Chaka, 2023) have fostered new types of interactions that can lower the barrier in human-machine communication through conversation in natural languages. We assume that such chatbots may be able to act as conversational assistants in tasks that otherwise require more complex processing, to improve the results produced by simpler or earlier, less performant techniques. This article proposes a set of small-scale tests with GenAI chatbots on post-OCR correction in historical datasets. It illustrates, through examples of responses obtained from GenAI agents integrated into post-OCR correction and assessment tasks, what types of challenges have to be addressed in this context when working with historical datasets. Previous studies have shown that OCR errors in input data can have non-negligible impact on downstream language processing, such as sentence segmentation, named entity recognition (NER), topic modelling and word embedding (Strien et al., 2020). Therefore, various methods for tackling this problem have been envisaged. For instance, machine learning techniques for automatically estimating text quality and selecting candidates for OCR rerun have been examined within cultural institutions that deal with historical data of lower quality (Schneider and Maurer, 2022). On the other hand, studies on post-OCR correction have investigated the use of pretrained language models, such as GPT-2 family, in combining different OCR views with the goal of producing fewer errors (Gupta et al., 2021). Our set of tests explore the potential of using GenAI agents in post-OCR correction. This type of enquiry is part of a larger project that uses word embedding results (neighbour lists) and citations extracted from a selection of historical French monographs (1690-1918)1 intended to be converted and integrated into a multilingual diachronic collection of interconnected terms expressed in RDF-XML to be published in the linguistic linked open data (LLOD) cloud (Armaselu et al., 2024). In this context, the need for OCR corrections of neighbour lists and citations has been identified. Methodology and results The example presented below has been produced using three chatbots, ChatGPT-4, Google Bard and YouChat that were selected based on availability via subscription and free accounts and on reports considering them among the top AI chatbots (Davis, 2023). The conversations were intended to identify potential problems in the lists of neighbours of the word révolution, such as OCR errors (Table 1). More complex prompting included several exchanges with the GenAI agent (ChatGPT-4) when asking for error identification in citations extracted from the corpus corresponding to a given sense of the word révolution 1 The MONOGRAPH TEXT PACK from the BnL Open Data Collection. https://data.bnl.lu/data/historical-newspapers/.