Zoekresultaten

Integration of Large Language Models in the Public Sector

An Exploration of LLM-Guided Detection of Discursive Patterns in Dutch Social Media

This paper presents a generative large language model (LLM)- guided approach to detect discursive patterns in Dutch social media. Newsrooms of municipalities and public organizations follow public debate on social media to be aware of and prepare for local and global issues and rumours. The onset of these issues and rumours are now detected by communication specialists in newsrooms. Using discourse analysis, we can ground their findings in theory. Devices from discursive psychology such as emotional evaluations are the lowest level components that can help spot and understand issues[ 1]. Thus, a rule-based NLP approach was developed to highlight these devices in a learning environment1. As a next step, we compare the rule-based approach to a large language modeling approach in order to assess the risks and benefits of both methods. We analyze the detection of two discursive patterns in Dutch tweets: magnifying (exaggerated) language use and assigning negative labels to persons or organizations. We compare the generated responses from two Dutch conversational LLMs finetuned on the Dutch language - Geitje-7B-Ultra and Fietje-2-Chat - and a rule-based NLP baseline using a two-fold evaluation process. The results show mixed performance, with the highest performing LLM setups yielding an accuracy of 64% for the maximizing language category and 73% for the negative labels to organizations/persons category. In comparison, the rule-based algorithm achieves an accuracy of 68% for both categories. Although the LLMs perform well in precision, they frequently find patterns in examples where no discursive markers were annotated. Moreover, the rationale analysis shows relatively poor results, pertaining to multiple factors including model size and interpretation of instructions. The results indicate that although there is merit in conducting discursive analysis using generative language models, it comes with the above risks. Recommendations for future work include combining the usage of language models with the rule-based setup for more robust detection as well as further indication of guidelines to improve upon the reasoning process.

Risk and Harm: Unpacking Ideologies in the AI Discourse

We examine the ideological differences in the debate surrounding large language models (LLMs) and AI regulation, focusing on the contrasting positions of the Future of Life Institute (FLI) and the Distributed AI Research (DAIR) institute. The study employs a humanistic HCI methodology, applying narrative theory to HCI-related topics and analyzing the political differences between FLI and DAIR, as they are brought to bear on research on LLMs. Two conceptual lenses, “existential risk” and “ongoing harm,” are applied to reveal differing perspectives on AI's societal and cultural significance. Adopting a longtermist perspective, FLI prioritizes preventing existential risks, whereas DAIR emphasizes addressing ongoing harm and human rights violations. The analysis further discusses these organizations’ stances on risk priorities, AI regulation, and attribution of responsibility, ultimately revealing the diverse ideological underpinnings of the AI and LLMs debate. Our analysis highlights the need for more studies of longtermism's impact on vulnerable populations, and we urge HCI researchers to consider the subtle yet significant differences in the discourse on LLMs.

Silicon sampling: AI-powered personas offer new insights for market research but have limitations

Thank you for sharing this story! However, please do so in a way that respects the copyright of this text. If you want to share or reproduce this full text, please ask permission from Innovation Origins (partners@innovationorigins.com) or become a partner of ours! You are of course free to quote this story with source citation. Would you like to share this article in another way? Then use this link to the article: https://innovationorigins.com/en/silicon-sampling-ai-powered-personas-offer-new-insights-for-market-research-but-have-limitations/ n the rapidly evolving field of marketing and communication, staying ahead means embracing technological innovations. The latest breakthrough, silicon sampling, leverages AI to revolutionize market research by creating synthetic personas that mimic human responses. This method, which utilizes large language models (LLMs) like GPT-4o, offers a cost-efficient and less time-consuming alternative to traditional market research. Roberta Vaznyte and Marieke van Vliet (Fontys University of Applied Science) have explored the promise and challenges of silicon sampling, highlighting key findings from recent experiments and the implications for the future of market research. 

Data Science Today and Tomorrow for SMEs

This final installment in our e-learning series offers a comprehensive look at the current impact and future potential of data science across industries. Using real-world examples like medical image analysis and operational efficiencies at Rotterdam The Hague Airport, we showcase data science’s transformative capabilities. The video also introduces the promise of Large Language Models (LLMs) such as Chat GPT and the simplification brought by Automated Machine Learning (AutoML). Emphasizing the blend of technology and human insight, we explore the evolving landscape of AI and data science for businesses.