8 citations · 17 across the 14 of their papers we have counts for
14 papers
Total Simulated Survey Error: Designing and Diagnosing Survey Responses from Large Language Models
Indira Sen, Georg Ahnert, Leah von der Heyde +3
Large Language models (LLMs), having been trained on vast amounts of human-generated data, may encode the attitudes and behaviors of these humans. As such, LLMs show promise in mim…
Missing the Margins: A Systematic Literature Review on the Demographic Representativeness of LLMs
Indira Sen, Marlene Lutz, Elisa Rogers +2
Many applications of Large Language Models (LLMs) require them to either simulate people or offer personalized functionality, making the demographic representativeness of LLMs cruc…
Do Psychometric Tests Work for Large Language Models? Evaluation of Tests on Sexism, Racism, and Morality
Jana Jung, Marlene Lutz, Indira Sen +1
Psychometric tests are increasingly used to assess psychological constructs in large language models (LLMs). However, it remains unclear whether these tests -- originally developed…
The Prompt Makes the Person(a): A Systematic Evaluation of Sociodemographic Persona Prompting for Large Language Models
Marlene Lutz, Indira Sen, Georg Ahnert +2
Persona prompting is increasingly used in large language models (LLMs) to simulate views of various sociodemographic groups. However, how a persona prompt is formulated can signifi…
Only a Little to the Left: A Theory-grounded Measure of Political Bias in Large Language Models
Mats Faulborn, Indira Sen, Max Pellert +2
Prompt-based language models like GPT4 and LLaMa have been used for a wide variety of use cases such as simulating agents, searching for information, or for content analysis. For a…
Robustness and Confounders in the Demographic Alignment of LLMs with Human Perceptions of Offensiveness
Shayan Alipour, Indira Sen, Mattia Samory +1
Large language models (LLMs) are known to exhibit demographic biases, yet few studies systematically evaluate these biases across multiple datasets or account for confounding facto…
The Unseen Targets of Hate -- A Systematic Review of Hateful Communication Datasets
Zehui Yu, Indira Sen, Dennis Assenmacher +5
Machine learning (ML)-based content moderation tools are essential to keep online spaces free from hateful communication. Yet, ML tools can only be as capable as the quality of the…
People Make Better Edits: Measuring the Efficacy of LLM-Generated Counterfactually Augmented Data for Harmful Language Detection
Indira Sen, Dennis Assenmacher, Mattia Samory +3
NLP models are used in a variety of critical social computing tasks, such as detecting sexist, racist, or otherwise hateful content. Therefore, it is imperative that these models a…
Total Error Sheets for Datasets (TES-D) -- A Critical Guide to Documenting Online Platform Datasets
Leon Fröhling, Indira Sen, Felix Soldner +3
This paper proposes a template for documenting datasets that have been collected from online platforms for research purposes. The template should help to critically reflect on data…
Counterfactually Augmented Data and Unintended Bias: The Case of Sexism and Hate Speech Detection
Indira Sen, Mattia Samory, Claudia Wagner +1
Counterfactually Augmented Data (CAD) aims to improve out-of-domain generalizability, an indicator of model robustness. The improvement is credited with promoting core features of…
Insider Stories: Analyzing Internal Sustainability Efforts of Major US Companies from Online Reviews
Indira Sen, Daniele Quercia, Licia Capra +2
It is hard to establish whether a company supports internal sustainability efforts (ISEs) like gender equality, diversity, and general staff welfare, not least because of lack of m…
How Does Counterfactually Augmented Data Impact Models for Social Computing Constructs?
Indira Sen, Mattia Samory, Fabian Floeck +2
As NLP models are increasingly deployed in socially situated settings such as online abusive content detection, it is crucial to ensure that these models are robust. One way of imp…
"Call me sexist, but...": Revisiting Sexism Detection Using Psychological Scales and Adversarial Samples
Mattia Samory, Indira Sen, Julian Kohne +2
Research has focused on automated methods to effectively detect sexism online. Although overt sexism seems easy to spot, its subtle forms and manifold expressions are not. In this…
TED-On: A Total Error Framework for Digital Traces of Human Behavior on Online Platforms
Indira Sen, Fabian Floeck, Katrin Weller +2
Peoples' activities and opinions recorded as digital traces online, especially on social media and other web-based platforms, offer increasingly informative pictures of the public.…