Papers by Divyanshu Aggarwal
MEGAVERSE: Benchmarking Large Language Models Across Languages, Modalities, Models and Tasks (2024.naacl-long)
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Sanchit Ahuja, Divyanshu Aggarwal, Varun Gumma, Ishaan Watts, Ashutosh Sathe, Millicent Ochieng, Rishav Hada, Prachi Jain, Mohamed Ahmed, Kalika Bali, Sunayana Sitaram
| Challenge: | Several new LLMs have been introduced necessitating their evaluation on non-English languages. |
| Approach: | They perform a thorough evaluation of the non-English capabilities of SoTA LLMs by comparing them on the same set of multilingual datasets. |
| Outcome: | The proposed model outperforms models on multilingual datasets on 22 languages including low-resource African languages. |
MAPLE: Multilingual Evaluation of Parameter Efficient Finetuning of Large Language Models (2024.findings-acl)
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| Challenge: | Prior work on multilingual evaluation has shown that there is a large gap between the performance of Large Language Models on English and other languages. |
| Approach: | They propose to finetune Llama-2 and Mistral models on two datasets to determine their effect on model performance on six downstream tasks covering forty one languages. |
| Outcome: | The proposed model can improve on six multilingual tasks while degrading on high-resource languages. |
IndicXNLI: Evaluating Multilingual Inference for Indian Languages (2022.emnlp-main)
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| Challenge: | Indic NLP has made rapid advances in terms of corpora and pre-trained models, but benchmark datasets on standard NLU tasks are limited. |
| Approach: | They propose to use an NLI dataset for 11 Indic languages to test their accuracy. |
| Outcome: | The proposed dataset provides useful insights into the behaviour of pre-trained models for a diverse set of languages. |
Improving Consistency in LLM Inference using Probabilistic Tokenization (2025.findings-naacl)
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| Challenge: | Prior work has shown that probabilistic tokenizations can generate multiple tokenization of the same input string. |
| Approach: | They propose a method to leverage the multiple tokenization capabilities of modern LLM tokenizers. |
| Outcome: | The proposed method improves the self-consistency of large language models by generating multiple tokenizations. |
Exploring Two-Phase Continual Instruction Fine-tuning for Multilingual Adaptation in Large Language Models (2026.findings-acl)
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| Challenge: | A key challenge for Large Language Models (LLMs) is improving their Multilingual instruction-following ability over time without deteriorating their ability in languages they already excel at, typically English. |
| Approach: | They propose a two-phase Continual Fine-tuning setup to improve a model's Multilingual adaptability by comparing an English-only LLM with a multilingual instruction dataset. |
| Outcome: | The proposed model improves on two-phase Continual Fine-tuning (CFT) setups on a multilingual instruction dataset. |
Explanations for CommonsenseQA: New Dataset and Models (2021.acl-long)
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Shourya Aggarwal, Divyanshu Mandowara, Vishwajeet Agrawal, Dinesh Khandelwal, Parag Singla, Dinesh Garg
| Challenge: | a dataset called CommonsenseQA (CQA) was recently released to advance the research on common-sense question answering (QA) |
| Approach: | They propose to retrieve and generate explanations for a given question, correct answer choice, incorrect answer choices tuple from a dataset called CommonsenseQA. |
| Outcome: | The proposed model beats baseline model by 100% in F1 score and similarity score of 61.9 . |
Improving Cross Lingual Transfer by Pretraining with Active Forgetting (2025.emnlp-main)
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| Challenge: | Prior work has shown that encoder-only LLMs show impressive cross lingual transfer of their capabilities from English to other languages. |
| Approach: | They propose a pretraining strategy that uses active forgetting to achieve similar cross lingual transfer in decoder-only LLMs. |
| Outcome: | The proposed model improves cross lingual transfer capabilities on non-English languages despite being trained on English data. |