Papers by Arnav Arora
A Survey on Stance Detection for Mis- and Disinformation Identification (2022.findings-naacl)
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| Challenge: | Understanding attitudes expressed in texts plays an important role in systems for detecting false information online, be it misinformation (unintentionally false) or disinformation (intentional false information). |
| Approach: | They examine the relationship between stance detection and mis- and disinformation detection online and examine the results of previous studies. |
| Outcome: | The proposed task is a component of fact-checking, rumour detection, and detecting previously fact- checked claims, and is compared with other related tasks such as argumentation mining and sentiment analysis. |
Specializing Large Language Models to Simulate Survey Response Distributions for Global Populations (2025.naacl-long)
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| Challenge: | Prior work has focused on using large language models to simulate human behaviors . but, LLMs are known to generate erroneous, stereotypical, or overconfident answers . |
| Approach: | They propose to specialize large language models for simulating survey response distributions by first-token probabilities. |
| Outcome: | The proposed model outperforms other methods and zero-shot classifiers on unseen questions, countries, and a completely unseened survey. |
Investigating Human Values in Online Communities (2025.naacl-long)
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| Challenge: | Existing value frameworks struggle with sample sizes and rely on selfreported surveys to calculate values. |
| Approach: | They propose a method to computationally analyse values on Reddit using in-domain and out-of-domain human annotations to train a value relevance and a polarity classifier. |
| Outcome: | The proposed method can be used to analyse values on reddit using human annotations and human annotation. |
Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers (2024.findings-emnlp)
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Yuxia Wang, Revanth Gangi Reddy, Zain Mujahid, Arnav Arora, Aleksandr Rubashevskii, Jiahui Geng, Osama Mohammed Afzal, Liangming Pan, Nadav Borenstein, Aditya Pillai, Isabelle Augenstein, Iryna Gurevych, Preslav Nakov
| Challenge: | Large language models generate naturally sounding answers over a broad range of human inquiries, but they often generate answers that contradict real-world facts. |
| Approach: | They propose a framework for annotating and evaluating the factuality of large language models . they propose 'factcheck-bench' which provides a multi-stage annotation scheme . |
| Outcome: | The proposed framework outperforms several popular LLM fact-checkers in claim, sentence, and document levels. |
Topic-Guided Sampling For Data-Efficient Multi-Domain Stance Detection (2023.acl-long)
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| Challenge: | Stance Detection is a task that aims to identify the attitudes of an author towards a target of interest. |
| Approach: | They propose a topic-guided diversity sampling technique and a contrastive objective to improve stance detection using the produced set. |
| Outcome: | The proposed method outperforms the state-of-the-art on 16 datasets with in-domain and out-of domain evaluations and is more generalizable with an averaged 10.2 F1 on out-domain evaluation. |
Why Should This Article Be Deleted? Transparent Stance Detection in Multilingual Wikipedia Editor Discussions (2023.emnlp-main)
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| Challenge: | Currently, only 20% of the English comments explicitly mention content moderation policies, but as few as 2% of the German and Turkish comments. |
| Approach: | They propose to use a multilingual dataset to predict stances with existing content moderation policies and to use them to explain moderation decisions. |
| Outcome: | The proposed model predicts stances and corresponding reasons with high accuracy, adding transparency to the decision-making process. |
A Reality Check on Context Utilisation for Retrieval-Augmented Generation (2025.acl-long)
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Lovisa Hagström, Sara Vera Marjanovic, Haeun Yu, Arnav Arora, Christina Lioma, Maria Maistro, Pepa Atanasova, Isabelle Augenstein
| Challenge: | Existing studies on LM context utilisation of retrieved information have focused on synthetic text. |
| Approach: | They propose a dataset of unreliable, insufficient and difficult-to-understand contexts with real-world queries and contexts manually annotated for stance to compare them to synthetic datasets. |
| Outcome: | The proposed model outperforms synthetic datasets and exaggerates rare context characteristics, leading to inflated context utilisation results. |
Cross-Domain Label-Adaptive Stance Detection (2021.emnlp-main)
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| Challenge: | Stance detection is a task that focuses on the classification of a writer’s viewpoint towards a target. |
| Approach: | They propose an end-to-end unsupervised framework for out-of-domain prediction of unseen, user-defined labels. |
| Outcome: | The proposed framework shows that it can be used to predict unseen labels over strong baselines. |
Thorny Roses: Investigating the Dual Use Dilemma in Natural Language Processing (2023.findings-emnlp)
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| Challenge: | Dual use is a problem in the context of natural language processing, says aaron eliotta . eelisa et al.: it is important to examine their rightful use and potential misuse . |
| Approach: | They propose a definition and checklist for dual-use in natural language processing based on a survey of NLP researchers and practitioners. |
| Outcome: | The proposed checklist focuses on dual-use in NLP based on a survey of NLP researchers and practitioners. |
Presumed Cultural Identity: How Names Shape LLM Responses (2025.findings-emnlp)
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| Challenge: | Names can be used as markers of individuality, cultural heritage, and personal history when interacting with chatbots. |
| Approach: | They propose to use names as cultural bias in chatbots to adapt to user input and task contexts. |
| Outcome: | The proposed method demonstrates that LLMs make cultural identity assumptions based on their users’ presumed backgrounds based upon their names . |
Multi-Modal Framing Analysis of News (2025.emnlp-main)
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| Challenge: | Automated frame analysis of political communication has been limited by the use of predefined frames and the visual contexts in which they appear. |
| Approach: | They propose a method for doing multi-modal, multi-label framing analysis at scale using large (vision-) language models. |
| Outcome: | The proposed method provides a more complete picture for understanding media bias. |
How Value Induction Reshapes LLM Behavior (2026.findings-acl)
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| Challenge: | Induction of values into LLMs can have unintended effects on the user interacting with it. |
| Approach: | They investigate the unintended effects of value incorporation into models by fine-tuning existing preference datasets and measuring their effect on safety, anthropomorphism and QA benchmarks. |
| Outcome: | The proposed model improves safety, anthropomorphism and QA benchmarks by inducing values and incorporating values into the model. |
LLM Tropes: Revealing Fine-Grained Values and Opinions in Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing approaches to evaluate latent values and opinions in large language models suffer from three notable shortcomings. |
| Approach: | They propose to analyze 156k LLM responses to 62 propositions of the Political Compass Test (PCT) generated by 6 LLMs using 420 prompt variations. |
| Outcome: | The proposed analysis of 156k LLM responses to the Political Compass Test (PCT) generated by 6 LLMs shows that tropes are recurrent and consistent across prompts. |