Papers by Vivek Kumar
mEdIT: Multilingual Text Editing via Instruction Tuning (2024.naacl-long)
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| Challenge: | mEdIT is a multi-lingual extension to CoEdit for writing assistance. |
| Approach: | They propose to train multi-lingual large language models (LLMs) by fine-tuning them via instruction tuning. |
| Outcome: | The proposed model performs well on multilingual text editing benchmarks and generalizes well to new languages. |
Adversarial Examples for Evaluating Math Word Problem Solvers (2021.findings-emnlp)
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| Challenge: | Existing MWP solvers do not understand language and its relation with numbers, and their accuracy is unclear. |
| Approach: | They propose two methods to generate adversarial attacks to evaluate the robustness of existing MWP solvers. |
| Outcome: | The proposed method reduces the accuracy of existing MWP solvers by over 40% on two benchmark datasets. |
Realistic Data Augmentation Framework for Enhancing Tabular Reasoning (2022.findings-emnlp)
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| Challenge: | Existing approaches to constructing training data for Natural Language Inference (NLI) tasks are expensive and time consuming. |
| Approach: | They propose a semi-automated framework for data augmentation for tabular inference . framework generates hypothesis templates transferable to similar tables . authors say framework could generate human-like tabular examples . |
| Outcome: | The proposed framework generates human-like tabular inference examples . it is based on human-written constraints and premise paraphrasing . |
DIAGRAMS : A Review Framework for Reasoning-Level Attribution in Diagram QA (2026.acl-demo)
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Anirudh Iyengar Kaniyar Narayana Iyengar, Tampu Ravi Kumar, Manan Suri, Raviteja Bommireddy, Dinesh Manocha, Puneet Mathur, Vivek Gupta
| Challenge: | Diagram question answering (Diagram QA) requires reasoning-level attribution that links each question-answer pair to all visual regions needed to derive the answer. |
| Approach: | They propose a diagram question-answer review framework that decouples interface logic from dataset-specific JSON structures through an internal meta-schema and dataset adapters. |
| Outcome: | The proposed framework achieves 85.39% precision and 75.30% recall across six diagram QA datasets. |
Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages (2022.tacl-1)
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Gowtham Ramesh, Sumanth Doddapaneni, Aravinth Bheemaraj, Mayank Jobanputra, Raghavan AK, Ajitesh Sharma, Sujit Sahoo, Harshita Diddee, Mahalakshmi J, Divyanshu Kakwani, Navneet Kumar, Aswin Pradeep, Srihari Nagaraj, Kumar Deepak, Vivek Raghavan, Anoop Kunchukuttan, Pratyush Kumar, Mitesh Shantadevi Khapra
| Challenge: | We present Samanantar, the largest publicly available parallel corpora collection for Indic languages . based on existing corporative, there has been limited benefit for resource-poor languages despite the lack of parallel corporals and monolingual corporata. |
| Approach: | They compile 12.4 million sentence pairs from existing corpora and mine 37.4 million from the Web. |
| Outcome: | The proposed model outperforms existing models and benchmarks on public datasets. |
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. |
VeeAlign: Multifaceted Context Representation Using Dual Attention for Ontology Alignment (2021.emnlp-main)
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| Challenge: | State-of-the-art Ontology Alignment systems are based on domain-dependent approaches with handcrafted rules or domain-specific architectures, making them unscalable and inefficient. |
| Approach: | They propose a Deep Learning based model that exploits syntactic and semantic information encoded in ontologies by using a dual-attention mechanism. |
| Outcome: | The proposed model exploits syntactic and semantic information encoded in ontologies and is flexible and scalable to different domains with minimal effort. |
TabPert : An Effective Platform for Tabular Perturbation (2021.emnlp-demo)
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| Challenge: | Current transformers-based models outperform humans on factual evidence evaluation tasks when presented as simple unstructured text. |
| Approach: | TabPert generates counterfactual data to assess model tabular reasoning issues. |
| Outcome: | TabPert analyzes the model's shortcomings methodically and quantitatively. |
CourtEval: A Courtroom-Based Multi-Agent Evaluation Framework (2025.findings-acl)
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| Challenge: | Existing automated evaluation metrics like ROUGE and BLEU show low correlation with human judgments. |
| Approach: | They propose a multi-agent evaluation framework that integrates multiple agents . they use ROUGE and BLEU to evaluate natural language models . |
| Outcome: | The proposed evaluation framework outperforms the current state-of-the-art methods in two meta-evaluation benchmarks. |
Practice Makes a Solver Perfect: Data Augmentation for Math Word Problem Solvers (2022.naacl-main)
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| Challenge: | Existing Math Word Problem solvers do not generalize well and rely on superficial cues to achieve high performance. |
| Approach: | They propose several data augmentation techniques to increase the size of existing MWP datasets by five folds by deploying them to a benchmark dataset. |
| Outcome: | The proposed methods increase the generalization and robustness of existing solvers by over five percentage points on benchmark datasets. |
Ask the experts: sourcing a high-quality nutrition counseling dataset through Human-AI collaboration (2024.findings-emnlp)
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Simone Balloccu, Ehud Reiter, Karen Li, Rafael Sargsyan, Vivek Kumar, Diego Reforgiato, Daniele Riboni, Ondrej Dusek
| Challenge: | Large Language Models (LLMs) are being used by end-users for various tasks, including sensitive ones such as health counseling, disregarding potential safety concerns. |
| Approach: | They use ChatGPT to crowd-source dietary struggles and work with nutrition experts to generate supportive text using ChatGPS. |
| Outcome: | The proposed model outperforms other models on dietary struggles and mental health tasks. |