Crafting a Museum Guide Using ChatGPT4

Trichopoulos, Georgios; Konstantakis, Markos; Caridakis, George; Katifori, Akrivi; Koukouli, Myrto

doi:10.3390/bdcc7030148

Cited by 11 publications

(8 citation statements)

References 39 publications

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“…Digitised physical heritage assets (sites, buildings, and objects) can be utilised within a genAI environment while preserving provenance, authenticity, and veracity [63]. In the museum space, genAI language models can be successfully employed in the creation of exhibit labels and catalogue information [64], developing visitor query systems and guides for museums [65][66][67], conceptualising entire exhibitions [68], as well as exploring how to approach aspects such as memorialisation [69].…”

Section: Use Of Genai Language Models In Cultural Heritage Studiesmentioning

confidence: 99%

Will Artificial Intelligence Affect How Cultural Heritage Will Be Managed in the Future? Responses Generated by Four genAI Models

Spennemann

2024

Heritage

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Generative artificial intelligence (genAI) language models have become firmly embedded in public consciousness. Their abilities to extract and summarise information from a wide range of sources in their training data have attracted the attention of many scholars. This paper examines how four genAI large language models (ChatGPT, GPT4, DeepAI, and Google Bard) responded to prompts, asking (i) whether artificial intelligence would affect how cultural heritage will be managed in the future (with examples requested) and (ii) what dangers might emerge when relying heavily on genAI to guide cultural heritage professionals in their actions. The genAI systems provided a range of examples, commonly drawing on and extending the status quo. Without a doubt, AI tools will revolutionise the execution of repetitive and mundane tasks, such as the classification of some classes of artifacts, or allow for the predictive modelling of the decay of objects. Important examples were used to assess the purported power of genAI tools to extract, aggregate, and synthesize large volumes of data from multiple sources, as well as their ability to recognise patterns and connections that people may miss. An inherent risk in the ‘results’ presented by genAI systems is that the presented connections are ‘artifacts’ of the system rather than being genuine. Since present genAI tools are unable to purposively generate creative or innovative thoughts, it is left to the reader to determine whether any text that is provided by genAI that is out of the ordinary is meaningful or nonsensical. Additional risks identified by the genAI systems were that some cultural heritage professionals might use AI systems without the required level of AI literacy and that overreliance on genAI systems might lead to a deskilling of general heritage practitioners.

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Section: Use Of Genai Language Models In Cultural Heritage Studiesmentioning

confidence: 99%

Will Artificial Intelligence Affect How Cultural Heritage Will Be Managed in the Future? Responses Generated by Four genAI Models

Spennemann

2024

Heritage

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“…Earlier models, including GPT-3, introduced in 2020 [12], and PaLM, in 2022 [13], showcased the capabilities of LLMs in various NLP tasks. By the end of November 2022, ChatGPT [14], a variant of the GPT-3 model [12], emerged as a pivotal and often debated milestone in LLM evolution [15]. However, ChatGPT is not only an expansion of existing models.…”

Section: Large Language Modelsmentioning

confidence: 99%

AI-Generated Text Detector for Arabic Language Using Encoder-Based Transformer Architecture

Alshammari,

El-Sayed,

Elleithy

2024

BDCC

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The effectiveness of existing AI detectors is notably hampered when processing Arabic texts. This study introduces a novel AI text classifier designed specifically for Arabic, tackling the distinct challenges inherent in processing this language. A particular focus is placed on accurately recognizing human-written texts (HWTs), an area where existing AI detectors have demonstrated significant limitations. To achieve this goal, this paper utilized and fine-tuned two Transformer-based models, AraELECTRA and XLM-R, by training them on two distinct datasets: a large dataset comprising 43,958 examples and a custom dataset with 3078 examples that contain HWT and AI-generated texts (AIGTs) from various sources, including ChatGPT 3.5, ChatGPT-4, and BARD. The proposed architecture is adaptable to any language, but this work evaluates these models’ efficiency in recognizing HWTs versus AIGTs in Arabic as an example of Semitic languages. The performance of the proposed models has been compared against the two prominent existing AI detectors, GPTZero and OpenAI Text Classifier, particularly on the AIRABIC benchmark dataset. The results reveal that the proposed classifiers outperform both GPTZero and OpenAI Text Classifier with 81% accuracy compared to 63% and 50% for GPTZero and OpenAI Text Classifier, respectively. Furthermore, integrating a Dediacritization Layer prior to the classification model demonstrated a significant enhancement in the detection accuracy of both HWTs and AIGTs. This Dediacritization step markedly improved the classification accuracy, elevating it from 81% to as high as 99% and, in some instances, even achieving 100%.

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“…Moreover, in the last couple of years, the continuous development of GPT has brought about significant changes in various sectors, reshaping interactions with technology. Among the fields profoundly impacted by the advancements in GPT, CH research stands out prominently [12][13][14][15][16].…”

Section: Related Workmentioning

confidence: 99%

“…MAGICAL (Museum AI Guide for Augmenting Cultural Heritage with Intelligent Language model) [12] is a system based on GPT-4 that acts as a digital tour guide in museums. It uses the capabilities of the language model to compose texts and create dialogues with the visitor.…”

Section: Magical As a Recommendation Systemmentioning

confidence: 99%

Large Language Models as Recommendation Systems in Museums

Trichopoulos,

Konstantakis,

Alexandridis

et al. 2023

Electronics

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This paper proposes the utilization of large language models as recommendation systems for museum visitors. Since the aforementioned models lack the notion of context, they cannot work with temporal information that is often present in recommendations for cultural environments (e.g., special exhibitions or events). In this respect, the current work aims to enhance the capabilities of large language models through a fine-tuning process that incorporates contextual information and user instructions. The resulting models are expected to be capable of providing personalized recommendations that are aligned with user preferences and desires. More specifically, Generative Pre-trained Transformer 4, a knowledge-based large language model is fine-tuned and turned into a context-aware recommendation system, adapting its suggestions based on user input and specific contextual factors such as location, time of visit, and other relevant parameters. The effectiveness of the proposed approach is evaluated through certain user studies, which ensure an improved user experience and engagement within the museum environment.

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Crafting a Museum Guide Using ChatGPT4

Cited by 11 publications

References 39 publications

Will Artificial Intelligence Affect How Cultural Heritage Will Be Managed in the Future? Responses Generated by Four genAI Models

Will Artificial Intelligence Affect How Cultural Heritage Will Be Managed in the Future? Responses Generated by Four genAI Models

AI-Generated Text Detector for Arabic Language Using Encoder-Based Transformer Architecture

Large Language Models as Recommendation Systems in Museums

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