Papers by Anoop Kumar
IndicNLG Benchmark: Multilingual Datasets for Diverse NLG Tasks in Indic Languages (2022.emnlp-main)
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Aman Kumar, Himani Shrotriya, Prachi Sahu, Amogh Mishra, Raj Dabre, Ratish Puduppully, Anoop Kunchukuttan, Mitesh M. Khapra, Pratyush Kumar
| Challenge: | IndicNLG is a non-English language that is hampered by the scarcity of datasets. |
| Approach: | They propose to create a dataset for natural language generation for 11 Indic languages . they use a set of pre-trained models to train multilingual models . |
| Outcome: | The proposed datasets show that pre-trained models perform well in multilingual and monolingual tasks. |
Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languages (2023.acl-long)
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Arnav Mhaske, Harshit Kedia, Sumanth Doddapaneni, Mitesh M. Khapra, Pratyush Kumar, Rudra Murthy, Anoop Kunchukuttan
| Challenge: | Named Entity Recognition (NER) is a fundamental task in natural language processing (NLP). |
| Approach: | They present the largest publicly available Named Entity Recognition dataset for the 11 major Indian languages from two language families. |
| Outcome: | The proposed dataset is the largest publicly available Named Entity Recognition (NER) dataset for the 11 major Indian languages from two language families. |
ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation (2023.acl-long)
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| Challenge: | Paraphrase generation is a long-standing task in natural language processing (NLP). |
| Approach: | They propose to generate large-scale syntactically diverse paraphrase datasets by abstract meaning representation back-translation. |
| Outcome: | The proposed dataset is syntactically more diverse than existing datasets while maintaining good semantic similarity. |
Harmonizing Diverse Models: A Layer-wise Merging Strategy for Consistent Generation (2025.emnlp-industry)
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| Challenge: | RAG systems often generate inconsistent outputs for semantically equivalent inputs . this unpredictability undermines the reliability of RAG and poses challenges for adoption in high-stakes or knowledge-sensitive domains such as finance, healthcare, and scientific research. |
| Approach: | They propose a method that integrates knowledge from specialized models into a single model to improve output consistency. |
| Outcome: | The proposed model significantly improves output consistency, achieving approximately 47.5% improvement in response similarity over baseline. |
Aksharantar: Open Indic-language Transliteration datasets and models for the Next Billion Users (2023.findings-emnlp)
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Yash Madhani, Sushane Parthan, Priyanka Bedekar, Gokul Nc, Ruchi Khapra, Anoop Kunchukuttan, Pratyush Kumar, Mitesh Khapra
| Challenge: | Indian subcontinent is home to diverse languages written in multiple scripts . widespread use of romanization and lack of standardization means accurate transliteration models form a critical component in the NLP stack for Indian languages used by over 735 million Internet users. |
| Approach: | They propose to build a transliteration dataset using monolingual and parallel corpora and human annotators. |
| Outcome: | The proposed model improves accuracy by 15% on the Dakshina test set and establishes strong baselines on the Aksharantar test set. |
IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages (2020.findings-emnlp)
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Divyanshu Kakwani, Anoop Kunchukuttan, Satish Golla, Gokul N.C., Avik Bhattacharyya, Mitesh M. Khapra, Pratyush Kumar
| Challenge: | In this paper, we present NLP resources for 11 major Indian languages . distributional representations are the cornerstone of modern NLP, authors say . |
| Approach: | They introduce NLP resources for 11 major Indian languages from two major language families . monolingual corpora contains 8.8 billion tokens across all 11 languages and Indian English . they also compile a benchmark for Indian language NLU to evaluate their results . |
| Outcome: | The monolingual corpora contains 8.8 billion tokens across all 11 languages and Indian English . the pre-trained language models are based on the compact ALBERT model . |
Neural Architecture Search for Parameter-Efficient Fine-tuning of Large Pre-trained Language Models (2023.findings-acl)
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| Challenge: | Parameter-efficient tuning (PET) methods fit pre-trained language models to downstream tasks by computing a small compressed update for a subset of parameter updates or appending and fine-tuning a few new parameters to the pre-train network. |
| Approach: | They propose a parameter-efficient tuning method that prunes PET architectures using structured pruning. |
| Outcome: | The proposed method performs well in practice but could be improved via NAS . it can be used to fine-tune large pre-trained language models on multiple tasks . |
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. |
Towards Leaving No Indic Language Behind: Building Monolingual Corpora, Benchmark and Models for Indic Languages (2023.acl-long)
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Sumanth Doddapaneni, Rahul Aralikatte, Gowtham Ramesh, Shreya Goyal, Mitesh M. Khapra, Anoop Kunchukuttan, Pratyush Kumar
| Challenge: | Recent advances in Natural Language Understanding are driven by pretrained multilingual models, which can potentially reduce the performance gap between high-resource languages through zero-shot knowledge transfer. |
| Approach: | They propose to create a human-supervised benchmark for Indic languages, IndicXTREME, with nine diverse NLU tasks covering 20 languages. |
| Outcome: | The proposed model improves on the monolingual corpora, IndicCorp, and IndicBERT in Indic languages with 105 evaluation sets across languages and tasks. |
TruthTorchLM: A Comprehensive Library for Predicting Truthfulness in LLM Outputs (2025.emnlp-demos)
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Duygu Nur Yaldiz, Yavuz Faruk Bakman, Sungmin Kang, Alperen Öziş, Hayrettin Eren Yildiz, Mitash Ashish Shah, Zhiqi Huang, Anoop Kumar, Alfy Samuel, Daben Liu, Sai Praneeth Karimireddy, Salman Avestimehr
| Challenge: | Generative Large Language Models (LLMs) produce untruthful outputs, referred to as hallucinations, which are often referred as false positives. |
| Approach: | They propose an open-source Python library with over 30 truthfulness prediction methods. |
| Outcome: | The proposed methods span diverse trade-offs in computational cost, access level, grounding document requirements, and supervision type (self-supervised or supervised). |
Prompt Perturbation Consistency Learning for Robust Language Models (2024.findings-eacl)
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Yao Qiang, Subhrangshu Nandi, Ninareh Mehrabi, Greg Ver Steeg, Anoop Kumar, Anna Rumshisky, Aram Galstyan
| Challenge: | Large language models have demonstrated impressive performance on a number of natural language processing tasks, such as question answering and text summarization. |
| Approach: | They propose a method to reduce the performance drop of large language models by regularizing the divergence between losses from clean and perturbed samples. |
| Outcome: | The proposed approach recovers on average 59% and 69% of the performance drop for IC and SF tasks while using ten times fewer augmented data samples. |
Empowering Multi-Turn Tool-Integrated Agentic Reasoning with Group Turn Policy Optimization (2026.acl-long)
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Yifeng Ding, Hung Le, Songyang Han, Kangrui Ruan, Zhenghui Jin, Varun Kumar, Zijian Wang, Anoop Deoras
| Challenge: | Current reinforcement learning methods suffer from coarse-grained, trajectory-level rewards that provide insufficient learning signals for complex multi-turn interactions, leading to training stagnation. |
| Approach: | They propose a novel RL algorithm for training large language models for multi-turn tool-integrated reasoning (TIR) that incorporates three innovations: turn-level reward assignment that provides fine-grained feedback for individual turns, return-based advantage estimation where normalized discounted returns are calculated as advantages, and self-supervised reward shaping that exploits self-supervision signals from generated code to densify sparse binary outcome-based rewards. |
| Outcome: | The proposed algorithm outperforms GRPO by 3.0% across diverse math reasoning benchmarks and improves grepo by 3.9% on commonsense reasoning and program synthesis tasks. |
IndicBART: A Pre-trained Model for Indic Natural Language Generation (2022.findings-acl)
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| Challenge: | IndicBART is a multilingual, sequence-to-sequence pre-trained model focusing on 11 Indic languages and English. |
| Approach: | They present a multilingual sequence-to-sequence pre-trained model for Indic languages . they evaluate it on two NLG tasks: Neural Machine Translation and extreme summarization . |
| Outcome: | The proposed model performs well on low-resource translation scenarios . Script sharing, multilingual training, and better utilization contribute to the performance. |
Agenda-Driven Question Generation: A Case Study in the Courtroom Domain (2024.lrec-main)
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| Challenge: | Existing automated question generation methods focus on unstructured text and lack agenda and background documents as context. |
| Approach: | They propose to leverage large language models for CourtQG by fine-tuning them on two auxiliary tasks, agenda explanation and question type prediction. |
| Outcome: | The proposed method generates better questions according to standard metrics when compared to several baselines. |
Unsupervised Syntactically Controlled Paraphrase Generation with Abstract Meaning Representations (2022.findings-emnlp)
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| Challenge: | Existing approaches to syntactically controlled paraphrase generation require annotated paraphrase pairs for training and are costly to extend to new domains. |
| Approach: | They propose to leverage Abstract Meaning Representations (AMR) to improve the performance of unsupervised syntactically controlled paraphrase generation. |
| Outcome: | The proposed model generates more accurate syntactically controlled paraphrases, both quantitatively and qualitatively, compared to the existing unsupervised approaches. |
IndicMT Eval: A Dataset to Meta-Evaluate Machine Translation Metrics for Indian Languages (2023.acl-long)
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Ananya Sai B, Tanay Dixit, Vignesh Nagarajan, Anoop Kunchukuttan, Pratyush Kumar, Mitesh M. Khapra, Raj Dabre
| Challenge: | Recent studies on machine translation systems focus on high-resource languages, but focus has shifted to low-resourced languages. |
| Approach: | They evaluate 16 metrics from a multidimensional quality metric dataset . they show pre-trained metrics have higher correlations with annotator scores . |
| Outcome: | The proposed evaluations show that pre-trained metrics outperform COMET on Indian languages. |
A Comparison of Independent and Joint Fine-tuning Strategies for Retrieval-Augmented Generation (2025.findings-emnlp)
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| Challenge: | Multiple fine-tuning strategies exist with different costs and benefits for RAG pipelines. |
| Approach: | They evaluate several RAG fine-tuning strategies with different costs and benefits . embedding and generator models can be fine- tuned to increase performance . |
| Outcome: | The proposed techniques improve quality metrics, but have different computational costs. |
Measuring and Mitigating Local Instability in Deep Neural Networks (2023.findings-acl)
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| Challenge: | Uncertain details like random initialization can change the outputs of a trained system with potentially disastrous consequences. |
| Approach: | They propose a model stability problem by studying how the predictions of a deep neural network change as a consequence of stochasticity in the training process. |
| Outcome: | The proposed method outperforms data-agnostic methods and is 90% cheaper than the gold standard. |
Temporal Generalization for Spoken Language Understanding (2022.naacl-industry)
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| Challenge: | Spoken Language Understanding models are usually trained offline on historical data, but must perform well on incoming user requests after deployment. |
| Approach: | They propose different strategies for achieving good temporal generalization . they focus on temporal drift, where the distribution of utterances may change . |
| Outcome: | The proposed model can perform well on unseen domains, e.g., upcoming data. |
An Automatic Method to Estimate Correctness of RAG (2025.coling-industry)
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| Challenge: | Existing methods to assess the correctness of RAG models fail to capture the model’s internal state during answer generation. |
| Approach: | They propose a method to predict the correctness of RAG models by modeling the model’s uncertainty on quantified perturbations of input. |
| Outcome: | Extensive experiments across multiple large language models show that the proposed approach quantifies RAG robustness by aligning predictions with ground truth with a MSE 0.002 while offering flexibility for diverse qualitative metrics. |
Correcting Language Model Outputs by Editing Salient Layers (2024.findings-eacl)
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| Challenge: | Existing models that use manual layer selection require prior domain knowledge and expensive empirical layer selection methods. |
| Approach: | They propose a model editing approach that selectively edits a small subset of model parameters to update the factual knowledge. |
| Outcome: | The proposed solution matches the accuracy of previous approaches with only 1/3 of their edits, enabling efficient updates to the parametric knowledge in large language models. |