Papers by Mitesh Khapra
Finding Blind Spots in Evaluator LLMs with Interpretable Checklists (2024.emnlp-main)
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| Challenge: | Large Language Models are increasingly relied upon to evaluate text outputs of other LLMs . however, concerns persist over the accuracy of these assessments and the potential for misleading conclusions. |
| Approach: | They propose a framework to assess the reliability of Large Language Models (LLMs) they propose ' FBI' framework to examine the proficiency of Evaluator LLMs in assessing four critical abilities . |
| Outcome: | The proposed framework assesses the performance of LLMs in text generation tasks. |
IndicVoices: Towards building an Inclusive Multilingual Speech Dataset for Indian Languages (2024.findings-acl)
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Tahir Javed, Janki Nawale, Eldho George, Sakshi Joshi, Kaushal Bhogale, Deovrat Mehendale, Ishvinder Sethi, Aparna Ananthanarayanan, Hafsah Faquih, Pratiti Palit, Sneha Ravishankar, Saranya Sukumaran, Tripura Panchagnula, Sunjay Murali, Kunal Gandhi, Ambujavalli R, Manickam M, C Vaijayanthi, Krishnan Karunganni, Pratyush Kumar, Mitesh Khapra
| Challenge: | Using INDICVOICES, we build the first ASR model to support all 22 languages listed in the 8th Schedule of the Constitution of India. |
| Approach: | They propose a dataset of natural and spontaneous speech from 16237 speakers covering 145 Indian districts and 22 languages. |
| Outcome: | The proposed dataset contains 7348 hours of read, extempore and conversational audio from 16237 speakers covering 145 Indian districts and 22 languages. |
How Good is Zero-Shot MT Evaluation for Low Resource Indian Languages? (2024.acl-short)
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| Challenge: | a recent study focused on machine translation evaluation for low-resource languages . linguistic aspects that vary across languages are factors that will exacerbate the problem in low-source languages due to the reliance on extensive data resources. |
| Approach: | They propose to use multi-dimensional quality metrics and DA annotations to meta-evaluate MT evaluation metrics for low-resource languages. |
| Outcome: | The proposed evaluation metrics are based on human scores on the candidate translations of assamese, maithili, and Punjabi. |
Active Evaluation: Efficient NLG Evaluation with Few Pairwise Comparisons (2022.acl-long)
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| Challenge: | Recent studies show that evaluating NLG systems using pairwise comparisons is expensive as the number of human annotations grows linearly with k. |
| Approach: | They propose a framework to efficiently identify the top-ranked system by actively choosing system pairs for comparison using dueling bandit algorithms. |
| Outcome: | The proposed framework reduces human annotations by 80% on 13 NLG evaluation datasets spanning 5 tasks . |
Input-specific Attention Subnetworks for Adversarial Detection (2022.findings-acl)
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| Challenge: | a new method to prune attention heads is proposed for adversarial detection . attention heads in models such as BERT are over-provisioned and can be pruned . |
| Approach: | They propose a method to construct input-specific attention subnetworks from which three features are extracted to discriminate between authentic and adversarial inputs. |
| Outcome: | The proposed method significantly improves state-of-the-art adversarial detection accuracy on 10 NLU datasets with 11 different adversarials. |
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. |
OpenHands: Making Sign Language Recognition Accessible with Pose-based Pretrained Models across Languages (2022.acl-long)
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| Challenge: | a new study examines the performance of pretraining for sign language recognition in low-resource settings. |
| Approach: | They propose using pose extracted through pretrained models as the standard modality of data to reduce training time and enable efficient inference. |
| Outcome: | The proposed model reduces training time and allows efficient inference in sign languages. |
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. |