Papers by Hemank Lamba
Uchaguzi-2022: A Dataset of Citizen Reports on the 2022 Kenyan Election (2025.coling-main)
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Roberto Mondini, Neema Kotonya, Robert L Logan IV, Elizabeth M. Olson, Angela Oduor Lungati, Daniel Odongo, Tim Ombasa, Hemank Lamba, Aoife Cahill, Joel Tetreault, Alejandro Jaimes
| Challenge: | Systematically organizing and geotagging large amounts of crowdsourced information requires substantial manual effort, often led by volunteers. |
| Approach: | They present a dataset of 14k citizen reports related to the 2022 Kenyan General Election . they investigate whether language models can assist in scalably categorizing and geotagging reports . |
| Outcome: | The proposed dataset aims to show whether language models can assist in categorizing and geotagging reports, thus highlighting its potential application in the AI for Social Good space. |
Explain then Rank: Scale Calibration of Neural Rankers Using Natural Language Explanations from LLMs (2025.findings-acl)
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| Challenge: | Neural ranking models produce the final document scores, but they are often treated as transient information and only the relative orderings are preserved to produce a ranking. |
| Approach: | They propose to exploit large language models (LLMs) to provide relevance and uncertainty signals for these neural text rankers to produce scale-calibrated scores through Monte Carlo sampling of natural language explanations (NLEs). |
| Outcome: | The proposed approach outperforms previous calibration methods and LLM-based methods for ranking, calibration, and query performance prediction tasks. |
Do LLMs Adhere to Label Definitions? Examining Their Receptivity to External Label Definitions (2025.emnlp-main)
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Seyedali Mohammadi, Bhaskara Hanuma Vedula, Hemank Lamba, Edward Raff, Ponnurangam Kumaraguru, Francis Ferraro, Manas Gaur
| Challenge: | Exact label definitions are considered as clues to disambiguate unclear labels, helping models perform their tasks more effectively. |
| Approach: | They conducted controlled experiments on multiple explanation benchmark datasets and label definition conditions using expert-curated, LLM-generated, perturbed, and swapped definitions. |
| Outcome: | The results suggest that models often default to internal representations, particularly in general tasks, while domain-specific tasks benefit more from explicit definitions. |
HumVI: A Multilingual Dataset for Detecting Violent Incidents Impacting Humanitarian Aid (2024.findings-emnlp)
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Hemank Lamba, Anton Abilov, Ke Zhang, Elizabeth Olson, Henry Dambanemuya, João Bárcia, David Batista, Christina Wille, Aoife Cahill, Joel Tetreault, Alejandro Jaimes
| Challenge: | Humanitarian organizations can analyze data to discover trends, gather aggregated insights, manage security risks, and inform advocacy and funding proposals. |
| Approach: | They present a dataset comprising news articles in three languages containing instances of different types of violent incidents categorized by the humanitarian sector they impact. |
| Outcome: | The proposed framework can be used to identify violent incidents and identify their impact on humanitarian operations. |
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. |
CEHA: A Dataset of Conflict Events in the Horn of Africa (2025.coling-main)
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Rui Bai, Di Lu, Shihao Ran, Elizabeth M. Olson, Hemank Lamba, Aoife Cahill, Joel Tetreault, Alejandro Jaimes
| Challenge: | Existing datasets categorizing conflict events do not cover all of the fine-grained types of conflict relevant to areas like the Horn of Africa. |
| Approach: | They propose to use online news articles to categorize violent conflict events . they propose to extract event-relevance and event-types from 500 English event descriptions . |
| Outcome: | The proposed dataset categorizes conflict risk according to specific areas required by stakeholders in the Humanitarian-Peace-Development Nexus. |