Papers by Akash Singh
M3Retrieve: Benchmarking Multimodal Retrieval for Medicine (2025.emnlp-main)
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| Challenge: | Strong retrieval models are increasingly important in knowledge-intensive domains. |
| Approach: | They propose a benchmark to evaluate multimodal retrieval models in medical settings . they examine 1.2 million text documents and 164K multimodal queries . |
| Outcome: | The proposed model spans 5 domains,16 medical fields, and 4 distinct tasks with over 1.2 Million text documents and 164K multimodal queries. |
How Robust Are the QA Models for Hybrid Scientific Tabular Data? A Study Using Customized Dataset (2024.lrec-main)
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| Challenge: | Existing tabular QA models are lacking in understanding their robustness on scientific information. |
| Approach: | They propose a dataset to assess the robustness of tabular QA models on scientific hybrid tabular data. |
| Outcome: | The proposed model performs well on scientific tables and text, while the best score is 0.462. |
Let’s Play Across Cultures: A Large Multilingual, Multicultural Benchmark for Assessing Language Models’ Understanding of Sports (2025.emnlp-main)
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Punit Kumar Singh, Nishant Kumar, Akash Ghosh, Kunal Pasad, Khushi Soni, Manisha Jaishwal, Sriparna Saha, Syukron Abu Ishaq Alfarozi, Asres Temam Abagissa, Kitsuchart Pasupa, Haiqin Yang, Jose G Moreno
| Challenge: | Language Models (LMs) are primarily evaluated on globally popular sports, often overlooking regional and indigenous sporting traditions. |
| Approach: | They propose to use multiple-choice questions (MCQs) to assess LMs' understanding of traditional sports across 60 countries and 6 continents. |
| Outcome: | The new benchmark will be publicly available, fostering research in culturally aware AI systems. |
The ComMA Dataset V0.2: Annotating Aggression and Bias in Multilingual Social Media Discourse (2022.lrec-1)
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Ritesh Kumar, Shyam Ratan, Siddharth Singh, Enakshi Nandi, Laishram Niranjana Devi, Akash Bhagat, Yogesh Dawer, Bornini Lahiri, Akanksha Bansal, Atul Kr. Ojha
| Challenge: | 59,152 comments are annotated with a hierarchical, fine-grained taget marking aggression and bias of various kinds on social media platforms. |
| Approach: | They propose to annotate a multilingual dataset with a hierarchical, fine-grained tagset marking different types of aggression and the "context" in which they occur. |
| Outcome: | The proposed dataset contains 59,152 comments in four languages, mostly code-mixed with English. |
MockingBERT: A Method for Retroactively Adding Resilience to NLP Models (2022.coling-1)
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| Challenge: | Existing remediations have compromised accuracy or required full model re-training with each new class of attacks. |
| Approach: | They propose a method of retroactively adding resilience to misspellings to transformer-based NLP models and propose generating adversarial misspells using an approximate method. |
| Outcome: | The proposed method significantly reduces the cost needed to evaluate a model’s resilience to adversarial attacks. |
RELIC: Enhancing Reward Model Generalization for Low-Resource Indic Languages with Few-Shot Examples (2025.findings-emnlp)
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Soumya Suvra Ghosal, Vaibhav Singh, Akash Ghosh, Soumyabrata Pal, Subhadip Baidya, Sriparna Saha, Dinesh Manocha
| Challenge: | a new reward model for low-resource Indic languages is proposed . a preference-based training approach is prohibitively expensive, authors say . |
| Approach: | a new in-context learning framework is proposed to train a retriever to select in-constext examples from low-resource Indic languages. |
| Outcome: | a new in-context learning framework for reward modeling in low-resource Indic languages is developed . the proposed framework outperforms existing examples on three preference datasets . |