9 citations · 36 across the 21 of their papers we have counts for
24 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…
How Value Induction Reshapes LLM Behaviour
Arnav Arora, Natalie Schluter, Katherine Metcalf +1
Conversational Large Language Models are post-trained on language that expresses specific behavioural traits, such as curiosity, open-mindedness, and empathy, and values, such as h…
Tailored untruths: How personalisation challenges LLM safeguards
João A. Leite, João Luz, Silvia Gargova +5
Large Language Models (LLMs) can generate highly persuasive disinformation, yet little is known about how effectively they personalise it across languages and demographic groups. W…
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…
A Reality Check on Context Utilisation for Retrieval-Augmented Generation
Lovisa Hagström, Sara Vera Marjanović, Haeun Yu +5
Retrieval-augmented generation (RAG) helps address the limitations of parametric knowledge embedded within a language model (LM). In real world settings, retrieved information can…
Survey of Cultural Awareness in Language Models: Text and Beyond
Siddhesh Pawar, Junyeong Park, Jiho Jin +7
Large-scale deployment of large language models (LLMs) in various applications, such as chatbots and virtual assistants, requires LLMs to be culturally sensitive to the user to ens…
Revealing Fine-Grained Values and Opinions in Large Language Models
Dustin Wright, Arnav Arora, Nadav Borenstein +3
Uncovering latent values and opinions embedded in large language models (LLMs) can help identify biases and mitigate potential harm. Recently, this has been approached by prompting…
Steady-state dynamics and non-local correlations in thermoelectric Cooper pair splitters
Arnav Arora, Siddhant Midha, Alexander Zyuzin +2
Recent experiments on Cooper pair splitters using superconductor-quantum dot hybrids have embarked on creating entanglement in the solid-state, by engineering the sub-gap processes…
Investigating Human Values in Online Communities
Nadav Borenstein, Arnav Arora, Lucie-Aimée Kaffee +1
Studying human values is instrumental for cross-cultural research, enabling a better understanding of preferences and behaviour of society at large and communities therein. To stud…
Overview of the 2023 ICON Shared Task on Gendered Abuse Detection in Indic Languages
Aatman Vaidya, Arnav Arora, Aditya Joshi +1
This paper reports the findings of the ICON 2023 on Gendered Abuse Detection in Indic Languages. The shared task deals with the detection of gendered abuse in online text. The shar…
The Uli Dataset: An Exercise in Experience Led Annotation of oGBV
Arnav Arora, Maha Jinadoss, Cheshta Arora +22
Online gender based violence has grown concomitantly with adoption of the internet and social media. Its effects are worse in the Global majority where many users use social media…
Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers
Yuxia Wang, Revanth Gangi Reddy, Zain Muhammad Mujahid +10
The increased use of large language models (LLMs) across a variety of real-world applications calls for mechanisms to verify the factual accuracy of their outputs. In this work, we…
Why Should This Article Be Deleted? Transparent Stance Detection in Multilingual Wikipedia Editor Discussions
Lucie-Aimée Kaffee, Arnav Arora, Isabelle Augenstein
The moderation of content on online platforms is usually non-transparent. On Wikipedia, however, this discussion is carried out publicly and the editors are encouraged to use the c…
Topic-Guided Sampling For Data-Efficient Multi-Domain Stance Detection
Erik Arakelyan, Arnav Arora, Isabelle Augenstein
Stance Detection is concerned with identifying the attitudes expressed by an author towards a target of interest. This task spans a variety of domains ranging from social media opi…
Thorny Roses: Investigating the Dual Use Dilemma in Natural Language Processing
Lucie-Aimée Kaffee, Arnav Arora, Zeerak Talat +1
Dual use, the intentional, harmful reuse of technology and scientific artefacts, is a problem yet to be well-defined within the context of Natural Language Processing (NLP). Howeve…
Probing Pre-Trained Language Models for Cross-Cultural Differences in Values
Arnav Arora, Lucie-Aimée Kaffee, Isabelle Augenstein
Language embeds information about social, cultural, and political values people hold. Prior work has explored social and potentially harmful biases encoded in Pre-Trained Language…
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…
Cross-Domain Label-Adaptive Stance Detection
Momchil Hardalov, Arnav Arora, Preslav Nakov +1
Stance detection concerns the classification of a writer's viewpoint towards a target. There are different task variants, e.g., stance of a tweet vs. a full article, or stance with…
A Survey on Stance Detection for Mis- and Disinformation Identification
Momchil Hardalov, Arnav Arora, Preslav Nakov +1
Understanding attitudes expressed in texts, also known as stance detection, plays an important role in systems for detecting false information online, be it misinformation (uninten…
Detecting Harmful Content On Online Platforms: What Platforms Need Vs. Where Research Efforts Go
Arnav Arora, Preslav Nakov, Momchil Hardalov +8
The proliferation of harmful content on online platforms is a major societal problem, which comes in many different forms including hate speech, offensive language, bullying and ha…
Multi-Hop Fact Checking of Political Claims
Wojciech Ostrowski, Arnav Arora, Pepa Atanasova +1
Recent work has proposed multi-hop models and datasets for studying complex natural language reasoning. One notable task requiring multi-hop reasoning is fact checking, where a set…