Papers by Angana Borah
Persuasion at Play: Understanding Misinformation Dynamics in Demographic-Aware Human-LLM Interactions (2026.eacl-long)
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| Challenge: | Existing challenges in misinformation exposure and susceptibility vary across demographics. |
| Approach: | They propose a framework that investigates the bidirectional persuasion dynamics between LLMs and humans when exposed to misinformation. |
| Outcome: | The proposed framework analyzes the spread of misinformation under persuasion among demographic-oriented LLM agents. |
The Power of Many: Multi-Agent Multimodal Models for Cultural Image Captioning (2025.naacl-long)
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| Challenge: | Large Multimodal Models exhibit impressive performance across multimodal tasks . effectiveness in cross-cultural contexts limited due to predominantly Western-centric nature of data and models . multi-agent models have shown significant capability in solving complex tasks despite limitations in crosscultural context . |
| Approach: | They propose to use a multi-agent framework to enhance cross-cultural image captioning using LMMs with distinct cultural personas to evaluate cultural information within image captions. |
| Outcome: | The proposed model outperforms single-agent models across different metrics and offers valuable insights for future research. |
Mind the (Belief) Gap: Group Identity in the World of LLMs (2025.findings-acl)
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| Challenge: | Social biases and belief-driven behaviors can significantly impact Large Language Models’ (LLMs) decisions on several tasks. |
| Approach: | They propose a multi-agent framework that simulates belief congruence, a group psychology theory that plays a crucial role in shaping societal interactions and preferences. |
| Outcome: | The proposed framework reduces misinformation dissemination and improves learning by 11% while reducing misinformation dissemination by up to 37%. |
Towards Implicit Bias Detection and Mitigation in Multi-Agent LLM Interactions (2024.findings-emnlp)
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| Challenge: | a recent study shows that large language models are susceptible to societal biases due to their exposure to human-generated data. |
| Approach: | They propose two strategies to mitigate implicit gender biases in large language models . they create scenarios where implicit gender is present and develop a metric to assess the presence of biase . |
| Outcome: | The proposed methods mitigate implicit biases with self-reflection and fine-tuning. |
NLP for Social Good: A Survey and Outlook of Challenges, Opportunities and Responsible Deployment (2026.eacl-long)
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Antonia Karamolegkou, Angana Borah, Eunjung Cho, Sagnik Ray Choudhury, Martina Galletti, Pranav Gupta, Oana Ignat, Priyanka Kargupta, Neema Kotonya, Hemank Lamba, Sun-Joo Lee, Arushi Mangla, Ishani Mondal, Fatima Zahra Moudakir, Deniz Nazar, Poli Nemkova, Dina Pisarevskaya, Naquee Rizwan, Nazanin Sabri, Keenan Samway, Dominik Stammbach, Anna Steinberg Schulten, David Tomás, Steven R Wilson, Bowen Yi, Jessica H Zhu, Arkaitz Zubiaga, Anders Søgaard, Alexander Fraser, Zhijing Jin, Rada Mihalcea, Joel R. Tetreault, Daryna Dementieva
| Challenge: | This paper surveys work in "NLP for Social Good" across nine domains relevant to global development and risk agendas. |
| Approach: | This paper analyzes work in "NLP for Social Good" across nine domains relevant to global development and risk agendas. |
| Outcome: | The paper analyzes work in "NLP for Social Good" across nine domains relevant to global development and risk agendas. |