LASIGE Talks are fortnightly/monthly events to publicize recently distinguished publications or ongoing cutting-edge work by researchers from the research centre, consolidating the scientific culture of the LASIGE community.

Speakers: Sérgio Alves (LASIGE-DI) and Samaneh Shafee (LASIGE)
Date: January 15th, 2025, Wednesday
Where: C6.3.27

Program:
11:45 Sérgio Alves
12:05 Samaneh Shafee
12:25 Q&A + Lunch

Talk1: Citizen-Led Personalization of User Interfaces: Investigating How People Customize Interfaces for Themselves and Others
Speaker: Sérgio Alves
User interface (UI) personalization can improve usability and user experience. However, current systems offer limited opportunities for customization, and third-party solutions often require significant effort and technical skills beyond the reach of most users. In our research, we explore the concept of UI customization for the self and others. We conducted a two-week study where nine participants utilized a custom-designed tool. This tool enables users to customize websites’ UI for themselves and to create and reply to customization assistance requests from others. In this talk, we present challenges and opportunities for future research seeking to democratize access to personalized UIs, particularly through community-based approaches.

Talk2: Evaluation of LLM Chatbots for OSINT-based Cyber Threat Awareness
Speaker: Samaneh Shafee
Knowledge sharing about emerging threats is crucial in the rapidly advancing field of cybersecurity and forms the foundation of Cyber Threat Intelligence (CTI). In this context, Large Language Models (LLMs) are becoming increasingly significant in the field of cybersecurity. Our study surveys the performance of ChatGPT, GPT4all, Dolly, Stanford Alpaca, Alpaca-LoRA, Falcon, and Vicuna LLM-based chatbots in binary classification and Named Entity Recognition (NER) tasks using Open-source intelligence (OSINT) to detect and extract structured data about cybersecurity threats. We utilize data collected in previous research from Twitter to assess the competitiveness of these chatbots when compared to specialized state-of-the-art models trained for those tasks.