Fabrizio Gilardi

Meysam Alizadeh, Maël Kubli, Zeynab Samei et al.

This paper studies the performance of open-source Large Language Models (LLMs) in text classification tasks typical for political science research. By examining tasks like stance, topic, and relevance classification, we aim to guide scholars in making informed decisions about their use of LLMs for text analysis. Specifically, we conduct an assessment of both zero-shot and fine-tuned LLMs across a range of text annotation tasks using news articles and tweets datasets. Our analysis shows that fine-tuning improves the performance of open-source LLMs, allowing them to match or even surpass zero-shot GPT-3.5 and GPT-4, though still lagging behind fine-tuned GPT-3.5. We further establish that fine-tuning is preferable to few-shot training with a relatively modest quantity of annotated text. Our findings show that fine-tuned open-source LLMs can be effectively deployed in a broad spectrum of text annotation applications. We provide a Python notebook facilitating the application of LLMs in text annotation for other researchers.

Fabrizio Gilardi, Meysam Alizadeh, Maël Kubli

Many NLP applications require manual data annotations for a variety of tasks, notably to train classifiers or evaluate the performance of unsupervised models. Depending on the size and degree of complexity, the tasks may be conducted by crowd-workers on platforms such as MTurk as well as trained annotators, such as research assistants. Using a sample of 2,382 tweets, we demonstrate that ChatGPT outperforms crowd-workers for several annotation tasks, including relevance, stance, topics, and frames detection. Specifically, the zero-shot accuracy of ChatGPT exceeds that of crowd-workers for four out of five tasks, while ChatGPT's intercoder agreement exceeds that of both crowd-workers and trained annotators for all tasks. Moreover, the per-annotation cost of ChatGPT is less than $0.003 -- about twenty times cheaper than MTurk. These results show the potential of large language models to drastically increase the efficiency of text classification.

Ana Kotarcic, Dominik Hangartner, Fabrizio Gilardi et al.

The shift of public debate to the digital sphere has been accompanied by a rise in online hate speech. While many promising approaches for hate speech classification have been proposed, studies often focus only on a single language, usually English, and do not address three key concerns: post-deployment performance, classifier maintenance and infrastructural limitations. In this paper, we introduce a new human-in-the-loop BERT-based hate speech classification pipeline and trace its development from initial data collection and annotation all the way to post-deployment. Our classifier, trained using data from our original corpus of over 422k examples, is specifically developed for the inherently multilingual setting of Switzerland and outperforms with its F1 score of 80.5 the currently best-performing BERT-based multilingual classifier by 5.8 F1 points in German and 3.6 F1 points in French. Our systematic evaluations over a 12-month period further highlight the vital importance of continuous, human-in-the-loop classifier maintenance to ensure robust hate speech classification post-deployment.

Emma Hoes, K. Jonathan Klueser, Fabrizio Gilardi

Digital platforms shape how people communicate, deliberate, and form opinions. Studying these dynamics has become increasingly difficult due to restricted data access, ethical constraints on real-world experiments, and limitations of existing research tools. VIRENA (Virtual Arena) is a platform that enables controlled experimentation in realistic social media environments. Multiple participants interact simultaneously in realistic replicas of feed-based platforms (Instagram, Facebook, Reddit) and messaging apps (WhatsApp, Messenger). Large language model-powered AI agents participate alongside humans with configurable personas and realistic behavior. Researchers can manipulate content moderation approaches, pre-schedule stimulus content, and run experiments across conditions through a visual interface requiring no programming skills. VIRENA makes possible research designs that were previously impractical: studying human--AI interaction in realistic social contexts, experimentally comparing moderation interventions, and observing group deliberation as it unfolds. Built on open-source technologies that ensure data remain under institutional control and comply with data protection requirements, VIRENA is currently in use at the University of Zurich and available for pilot collaborations. Designed for researchers, educators, and public organizations alike, VIRENA's no-code interface makes controlled social media simulation accessible across disciplines and sectors. This paper documents its design, architecture, and capabilities.

Fabrizio Gilardi, Sabrina Di Lorenzo, Juri Ezzaini et al.

The advancement of artificial intelligence has led to its application in many areas, including news media, which makes it crucial to understand public reception of AI-generated news. This preregistered study investigates (i) the perceived quality of AI-assisted and AI-generated versus human-generated news articles, (ii) whether disclosure of AI's involvement in generating these news articles influences engagement with them, and (iii) whether such awareness affects the willingness to read AI-generated articles in the future. We conducted a survey experiment with 599 Swiss participants, who evaluated the credibility, readability, and expertise of news articles either written by journalists (control group), rewritten by AI (AI-assisted group), or entirely written by AI (AI-generated group). Our results indicate that all articles were perceived to be of equal quality. When participants in the treatment groups were subsequently made aware of AI's role, they expressed a higher willingness to continue reading the articles than participants in the control group. However, they were not more willing to read AI-generated news in the future. These results suggest that aversion to AI usage in news media is not primarily rooted in a perceived lack of quality, and that by disclosing using AI, journalists could induce more short-term engagement.

Meysam Alizadeh, Zeynab Samei, Daria Stetsenko et al.

Previous benchmarks on prompt injection in large language models (LLMs) have primarily focused on generic tasks and attacks, offering limited insights into more complex threats like data exfiltration. This paper examines how prompt injection can cause tool-calling agents to leak personal data observed during task execution. Using a fictitious banking agent, we develop data flow-based attacks and integrate them into AgentDojo, a recent benchmark for agentic security. To enhance its scope, we also create a richer synthetic dataset of human-AI banking conversations. In 16 user tasks from AgentDojo, LLMs show a 15-50 percentage point drop in utility under attack, with average attack success rates (ASR) around 20 percent; some defenses reduce ASR to zero. Most LLMs, even when successfully tricked by the attack, avoid leaking highly sensitive data like passwords, likely due to safety alignments, but they remain vulnerable to disclosing other personal data. The likelihood of password leakage increases when a password is requested along with one or two additional personal details. In an extended evaluation across 48 tasks, the average ASR is around 15 percent, with no built-in AgentDojo defense fully preventing leakage. Tasks involving data extraction or authorization workflows, which closely resemble the structure of exfiltration attacks, exhibit the highest ASRs, highlighting the interaction between task type, agent performance, and defense efficacy.

Meysam Alizadeh, Fabrizio Gilardi, Zeynab Samei

As AI systems become pervasive, grounding their behavior in human values is critical. Prior work suggests that language models (LMs) exhibit limited inherent moral reasoning, leading to calls for explicit moral teaching. However, constructing ground truth data for moral evaluation is difficult given plural frameworks and pervasive biases. We investigate unsupervised elicitation as an alternative, asking whether pretrained (base) LMs possess intrinsic moral reasoning capability that can be surfaced without human supervision. Using the Internal Coherence Maximization (ICM) algorithm across three benchmark datasets and four LMs, we test whether ICM can reliably label moral judgments, generalize across moral frameworks, and mitigate social bias. Results show that ICM outperforms all pre-trained and chatbot baselines on the Norm Bank and ETHICS benchmarks, while fine-tuning on ICM labels performs on par with or surpasses those of human labels. Across theoretically motivated moral frameworks, ICM yields its largest relative gains on Justice and Commonsense morality. Furthermore, although chatbot LMs exhibit social bias failure rates comparable to their pretrained ones, ICM reduces such errors by more than half, with the largest improvements in race, socioeconomic status, and politics. These findings suggest that pretrained LMs possess latent moral reasoning capacities that can be elicited through unsupervised methods like ICM, providing a scalable path for AI alignment.

Meysam Alizadeh, Fabrizio Gilardi, Zeynab Samei et al.

Large language models (LLMs) have traditionally relied on static training data, limiting their knowledge to fixed snapshots. Recent advancements, however, have equipped LLMs with web browsing capabilities, enabling real time information retrieval and multi step reasoning over live web content. While prior studies have demonstrated LLMs ability to access and analyze websites, their capacity to directly retrieve and analyze social media data remains unexplored. Here, we evaluate whether web browsing LLMs can infer demographic attributes of social media users given only their usernames. Using a synthetic dataset of 48 X (Twitter) accounts and a survey dataset of 1,384 international participants, we show that these models can access social media content and predict user demographics with reasonable accuracy. Analysis of the synthetic dataset further reveals how LLMs parse and interpret social media profiles, which may introduce gender and political biases against accounts with minimal activity. While this capability holds promise for computational social science in the post API era, it also raises risks of misuse particularly in information operations and targeted advertising underscoring the need for safeguards. We recommend that LLM providers restrict this capability in public facing applications, while preserving controlled access for verified research purposes.

Valeria Vuk, Cristina Sarasua, Fabrizio Gilardi

A wide range of participation is essential for democracy, as it helps prevent the dominance of extreme views, erosion of legitimacy, and political polarization. However, engagement in online political discussions often features a limited spectrum of views due to high levels of self-selection and the tendency of online platforms to facilitate exchanges primarily among like-minded individuals. This study examines whether an LLM-based bot can widen the scope of perspectives expressed by participants in online discussions through two pre-registered randomized experiments conducted in a chatroom. We evaluate the impact of a bot that actively monitors discussions, identifies missing arguments, and introduces them into the conversation. The results indicate that our bot significantly expands the range of arguments, as measured by both objective and subjective metrics. Furthermore, disclosure of the bot as AI does not significantly alter these effects. These findings suggest that LLM-based moderation tools can positively influence online political discourse.

Dror Kris Markus, Fabrizio Gilardi, Daria Stetsenko

As artificial intelligence (AI) systems become deeply embedded in everyday life, calls to align AI development with ethical and societal values have intensified. Interdisciplinary collaboration is often championed as a key pathway for fostering such engagement. Yet it remains unclear whether interdisciplinary research teams are actually leading this shift in practice. This study analyzes over 100,000 AI-related papers published on ArXiv between 2014 and 2024 to examine how ethical values and societal concerns are integrated into technical AI research. We develop a classifier to identify societal content and measure the extent to which research papers express these considerations. We find a striking shift: while interdisciplinary teams remain more likely to produce societally-oriented research, computer science-only teams now account for a growing share of the field's overall societal output. These teams are increasingly integrating societal concerns into their papers and tackling a wide range of domains - from fairness and safety to healthcare and misinformation. These findings challenge common assumptions about the drivers of societal AI and raise important questions. First, what are the implications for emerging understandings of AI safety and governance if most societally-oriented research is being undertaken by exclusively technical teams? Second, for scholars in the social sciences and humanities: in a technical field increasingly responsive to societal demands, what distinctive perspectives can we still offer to help shape the future of AI?