4 citations · 7 across the 11 of their papers we have counts for
12 papers
Investigating Multi-Cultural Value Selection Biases in LLMs
Siddhesh Pawar, Arnav Arora, Dustin Wright +2
Large language models (LLMs) serve users whose cultural identities are multi-layered, yet cultural evaluations commonly reduce identity to one label. In this work, we study LLMs un…
Understanding the Interplay between LLMs' Utilisation of Parametric and Contextual Knowledge: A keynote at ECIR 2025
Isabelle Augenstein
Language Models (LMs) acquire parametric knowledge from their training process, embedding it within their weights. The increasing scalability of LMs, however, poses significant cha…
CulTrace: Tracing Internal Cultural Reasoning in Large Language Models
Haeun Yu, Arnav Arora, Seogyeong Jeong +7
The growing deployment of large language models (LLMs) across diverse cultural contexts necessitates a deeper understanding of models' hidden representations of different cultures.…
BiasGym: A Simple and Generalizable Framework for Analyzing and Removing Biases through Injection
Sekh Mainul Islam, Nadav Borenstein, Siddhesh Milind Pawar +3
Understanding biases and stereotypes encoded in the weights of Large Language Models (LLMs) is crucial for developing effective mitigation strategies. However, biased behavior is o…
Presumed Cultural Identity: How Names Shape LLM Responses
Siddhesh Pawar, Arnav Arora, Lucie-Aimée Kaffee +1
Names are deeply tied to human identity. They can serve as markers of individuality, cultural heritage, and personal history. However, using names as a core indicator of identity c…
Multi-Modal Framing Analysis of News
Arnav Arora, Srishti Yadav, Maria Antoniak +2
Automated frame analysis of political communication is a popular task in computational social science that is used to study how authors select aspects of a topic to frame its recep…
Specializing Large Language Models to Simulate Survey Response Distributions for Global Populations
Yong Cao, Haijiang Liu, Arnav Arora +3
Large-scale surveys are essential tools for informing social science research and policy, but running surveys is costly and time-intensive. If we could accurately simulate group-le…
Unstructured Evidence Attribution for Long Context Query Focused Summarization
Dustin Wright, Zain Muhammad Mujahid, Lu Wang +2
Large language models (LLMs) are capable of generating coherent summaries from very long contexts given a user query, and extracting and citing evidence spans helps improve the tru…
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…
Can Edge Probing Tasks Reveal Linguistic Knowledge in QA Models?
Sagnik Ray Choudhury, Nikita Bhutani, Isabelle Augenstein
There have been many efforts to try to understand what grammatical knowledge (e.g., ability to understand the part of speech of a token) is encoded in large pre-trained language mo…
Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training
Momchil Hardalov, Arnav Arora, Preslav Nakov +1
The goal of stance detection is to determine the viewpoint expressed in a piece of text towards a target. These viewpoints or contexts are often expressed in many different languag…
TX-Ray: Quantifying and Explaining Model-Knowledge Transfer in (Un-)Supervised NLP
Nils Rethmeier, Vageesh Kumar Saxena, Isabelle Augenstein
While state-of-the-art NLP explainability (XAI) methods focus on explaining per-sample decisions in supervised end or probing tasks, this is insufficient to explain and quantify mo…