no code implementations • 28 May 2024 • Somnath Kumar, Vaibhav Balloli, Mercy Ranjit, Kabir Ahuja, Tanuja Ganu, Sunayana Sitaram, Kalika Bali, Akshay Nambi
Second, we introduce a new hybrid approach that synergizes LLM Retrieval Augmented Generation (RAG) with multilingual embeddings and achieves improved multilingual task performance.
1 code implementation • 25 Apr 2024 • Kabir Ahuja, Vidhisha Balachandran, Madhur Panwar, Tianxing He, Noah A. Smith, Navin Goyal, Yulia Tsvetkov
Transformers trained on natural language data have been shown to learn its hierarchical structure and generalize to sentences with unseen syntactic structures without explicitly encoding any structural bias.
1 code implementation • 16 Mar 2024 • Fahim Faisal, Orevaoghene Ahia, Aarohi Srivastava, Kabir Ahuja, David Chiang, Yulia Tsvetkov, Antonios Anastasopoulos
This allows for a comprehensive evaluation of NLP system performance on different language varieties.
no code implementations • 12 Feb 2024 • Prachi Jain, Ashutosh Sathe, Varun Gumma, Kabir Ahuja, Sunayana Sitaram
In this work, we aim to modularly debias a pretrained language model across multiple dimensions.
no code implementations • 4 Jul 2023 • Aniket Vashishtha, Kabir Ahuja, Sunayana Sitaram
While understanding and removing gender biases in language models has been a long-standing problem in Natural Language Processing, prior research work has primarily been limited to English.
1 code implementation • 8 Jun 2023 • Madhur Panwar, Kabir Ahuja, Navin Goyal
One of the main discoveries in this line of research has been that for several function classes, such as linear regression, transformers successfully generalize to new functions in the class.
no code implementations • 28 May 2023 • Akshay Nambi, Vaibhav Balloli, Mercy Ranjit, Tanuja Ganu, Kabir Ahuja, Sunayana Sitaram, Kalika Bali
Our results show substantial advancements in multilingual understanding and generation across a diverse range of languages.
1 code implementation • 22 Mar 2023 • Kabir Ahuja, Harshita Diddee, Rishav Hada, Millicent Ochieng, Krithika Ramesh, Prachi Jain, Akshay Nambi, Tanuja Ganu, Sameer Segal, Maxamed Axmed, Kalika Bali, Sunayana Sitaram
Most studies on generative LLMs have been restricted to English and it is unclear how capable these models are at understanding and generating text in other languages.
1 code implementation • 21 Oct 2022 • Kabir Ahuja, Sunayana Sitaram, Sandipan Dandapat, Monojit Choudhury
Massively Multilingual Language Models (MMLMs) have recently gained popularity due to their surprising effectiveness in cross-lingual transfer.
no code implementations • NAACL 2022 • Kabir Ahuja, Monojit Choudhury, Sandipan Dandapat
Borrowing ideas from {\em Production functions} in micro-economics, in this paper we introduce a framework to systematically evaluate the performance and cost trade-offs between machine-translated and manually-created labelled data for task-specific fine-tuning of massively multilingual language models.
no code implementations • nlppower (ACL) 2022 • Kabir Ahuja, Sandipan Dandapat, Sunayana Sitaram, Monojit Choudhury
Although recent Massively Multilingual Language Models (MMLMs) like mBERT and XLMR support around 100 languages, most existing multilingual NLP benchmarks provide evaluation data in only a handful of these languages with little linguistic diversity.
no code implementations • ACL 2022 • Kabir Ahuja, Shanu Kumar, Sandipan Dandapat, Monojit Choudhury
Massively Multilingual Transformer based Language Models have been observed to be surprisingly effective on zero-shot transfer across languages, though the performance varies from language to language depending on the pivot language(s) used for fine-tuning.
no code implementations • COLING 2022 • Ishani Mondal, Kabir Ahuja, Mohit Jain, Jacki O Neil, Kalika Bali, Monojit Choudhury
The COVID-19 pandemic has brought out both the best and worst of language technology (LT).
1 code implementation • COLING 2020 • Satwik Bhattamishra, Kabir Ahuja, Navin Goyal
We find that while recurrent models generalize nearly perfectly if the lengths of the training and test strings are from the same range, they perform poorly if the test strings are longer.
1 code implementation • EMNLP 2020 • Satwik Bhattamishra, Kabir Ahuja, Navin Goyal
Our analysis also provides insights on the role of self-attention mechanism in modeling certain behaviors and the influence of positional encoding schemes on the learning and generalization abilities of the model.
2 code implementations • TACL 2020 • Ashutosh Kumar, Kabir Ahuja, Raghuram Vadapalli, Partha Talukdar
In these methods, syntactic-guidance is sourced from a separate exemplar sentence.