Papers by Rituraj Singh
NLMs: Augmenting Negation in Language Models (2023.findings-emnlp)
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| Challenge: | Negation is the fundamental component in a natural language that reverses the semantic meaning of a sentence. |
| Approach: | They propose a language model objective with a weighted cross-entropy loss and elastic weight consolidation regularization to improve negation understanding. |
| Outcome: | The proposed model reduces the error rate of the existing models by 8% and outperforms them on original and negation benchmarks. |
Constructing A Dataset of Support and Attack Relations in Legal Arguments in Court Judgements using Linguistic Rules (2022.lrec-1)
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| Challenge: | Argumentation mining is a growing area of research with several interesting practical applications. |
| Approach: | They propose three sets of rules based on linguistic knowledge and distant supervision to identify such relations from Indian Supreme Court judgments. |
| Outcome: | The proposed rules are based on linguistic knowledge and distant supervision and use the source of the argument to build a dataset of Support and Attack relations between sentences in a court judgement with reasonable accuracy. |
RG-VQA: Leveraging Retriever-Generator Pipelines for Knowledge Intensive Visual Question Answering (2025.findings-emnlp)
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Settaluri Lakshmi Sravanthi, Pulkit Agarwal, Debjyoti Mondal, Rituraj Singh, Subhadarshi Panda, Ankit Mishra, Kiran Pradeep, Srihari K B, Godawari Sudhakar Rao, Pushpak Bhattacharyya
| Challenge: | Existing methods to improve the reasoning capabilities of VQA systems are limited due to complexity of graph neural networks and end-to-end training. |
| Approach: | They propose a method to integrate Dense Passage Retrievers with Vision Language Models to boost the reasoning capabilities of VQA systems. |
| Outcome: | The proposed method outperforms human accuracy and GPT-4 in the ScienceQA dataset. |
From Perception to Reasoning: Enhancing Vision-Language Models for Mobile UI Understanding (2025.findings-acl)
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Settaluri Lakshmi Sravanthi, Ankit Mishra, Debjyoti Mondal, Subhadarshi Panda, Rituraj Singh, Pushpak Bhattacharyya
| Challenge: | Accurately grounding visual and textual elements within mobile user interfaces remains a challenge for Vision-Language Models (VLMs). |
| Approach: | They propose a mobile UI understanding model trained on a dataset specifically tailored for mobile screen understanding and grounding. |
| Outcome: | The proposed model achieves significant gains in accuracy across all perception tasks and on reasoning benchmarks. |
Nanda Family: Open-Weights Generative Large Language Models for Hindi (2026.eacl-long)
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Aaryamonvikram Singh, Debopriyo Banerjee, Dhruv Sahnan, Monojit Choudhury, Shivam Chauhan, Rocktim Jyoti Das, Xudong Han, Haonan Li, Alok Anil Jadhav, Utkarsh Agarwal, Mukund Choudhary, Fajri Koto, Junaid Hamid Bhat, Awantika Shukla, Samujjwal Ghosh, Samta Kamboj, Onkar Pandit, Lalit Pradhan, Rahul Pal, Sunil Kumar Sahu, Parvez Mullah, Ali El Filali, Zainul Abedien Ahmed Quraishi, Neha Sengupta, Gokulakrishnan Ramakrishnan, Rituraj Joshi, Gurpreet Gosal, Avraham Sheinin, Natalia Vassilieva, Preslav Nakov
| Challenge: | Large language models remain predominantly English-centric, which limits their utility for underrepresented languages. |
| Approach: | They propose to extend Llama’s vocabulary with 20% Hindi-specific tokens, thus halving Hindi tokenization fertility while preserving English efficiency. |
| Outcome: | The proposed models outperform open-weight models of comparable size on a 65B-token corpus and bilingual instruction and safety alignment on . a culturally grounded dataset. |