activity
20192026
most citedTX-Ray: Quantifying and Explaining Model-Knowledge Transfer in (Un-)Supervised NLP

4 citations · 7 across the 11 of their papers we have counts for

collaborators
Showing 2025Show all

6 papers · 1 filter

cs.CL2025

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.…

cs.CL2025

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…

cs.CL2025★ 1 cited

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…

cs.CL2025★ 1 cited

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…

cs.CL2025★ 1 cited

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…

cs.CL2025

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…