Papers by Kabir Ahuja
Multi Task Learning For Zero Shot Performance Prediction of Multilingual Models (2022.acl-long)
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| Challenge: | Massively Multilingual Transformer based Language Models have been shown to be effective on zero-shot transfer across languages, though performance varies from language to language depending on pivot language(s) used for fine-tuning. |
| Approach: | They propose to combine multi-task learning problems with multi-lingual Transformers to model zero-shot transfer across languages. |
| Outcome: | The proposed model can predict zero-shot transfer across languages with a multi-task learning problem with pretraining data in very few languages. |
Global Readiness of Language Technology for Healthcare: What Would It Take to Combat the Next Pandemic? (2022.coling-1)
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| Challenge: | Language Technology (LT) has been used in the COVID-19 pandemic, but only in a handful of languages. |
| Approach: | They propose to use conversational agents for information dissemination and basic diagnosis in 15 Asian and African languages with varying resource-availability to test their knowledge of LT. |
| Outcome: | The proposed research confirms the pitiful state of LT even for languages with large speaker bases, such as Sinhala and Hausa, and identifies the gaps that could help prioritize research and investment strategies in LT for healthcare. |
Syntax-Guided Controlled Generation of Paraphrases (2020.tacl-1)
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| Challenge: | Recent work has explored the incorporation of complex syntactic-guidance as constraints in the task of controlled text generation. |
| Approach: | They propose an end-to-end framework for controlled paraphrase generation that incorporates complex syntactic-guidance constraints into the task. |
| Outcome: | The proposed framework generates syntax-conforming sentences while not compromising on relevance. |
On the Economics of Multilingual Few-shot Learning: Modeling the Cost-Performance Trade-offs of Machine Translated and Manual Data (2022.naacl-main)
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| Challenge: | a framework to evaluate the performance and cost trade-offs between machine-translated and manually-created labelled data is presented. |
| Approach: | They propose a framework to 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. |
| Outcome: | The proposed framework can be used to evaluate cost trade-offs between machine-translated and manually-created labelled data for task-specific fine-tuning of massively multilingual models. |
On the Calibration of Massively Multilingual Language Models (2022.emnlp-main)
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| Challenge: | Massively Multilingual Language Models (MMLMs) have gained popularity due to their effectiveness in cross-lingual transfer. |
| Approach: | They investigate how well calibrated MMLMs are with respect to confidence . they find that calibration methods like temperature scaling and label smoothing improve calibration . |
| Outcome: | The proposed models are able to generalize in languages unseen during fine-tuning, but they are not reliable across languages. |
On the Ability and Limitations of Transformers to Recognize Formal Languages (2020.emnlp-main)
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| Challenge: | Existing studies on LSTMs have not revealed their ability to model syntactic properties. |
| Approach: | They propose to build a Transformers model for a subclass of counter languages and find that their learning mechanism strongly correlates with their construction. |
| Outcome: | The proposed model generalizes well on counter languages and its learned mechanism correlates with it. |
Learning Syntax Without Planting Trees: Understanding Hierarchical Generalization in Transformers (2025.tacl-1)
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Kabir Ahuja, Vidhisha Balachandran, Madhur Panwar, Tianxing He, Noah A. Smith, Navin Goyal, Yulia Tsvetkov
| Challenge: | Inductive biases in transformers can cause hierarchical generalization without explicitly encoding structural bias. |
| Approach: | They investigate sources of inductive bias in transformer models and their training that could cause such preference for hierarchical generalization. |
| Outcome: | The proposed model can generalize to novel syntactic forms without explicit bias . the proposed model is able to generalize on a dataset with a hierarchical grammar . |
MAFIA: Multi-Adapter Fused Inclusive Language Models (2024.eacl-long)
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| Challenge: | Pretrained Language Models (PLMs) are widely used in NLP for various tasks. |
| Approach: | They propose to modularly debias a pre-trained language model across multiple bias dimensions using structured knowledge and a large generative model. |
| Outcome: | The proposed model is able to debias a pre-trained language model across multiple bias dimensions in a semi-automated way. |
MEGA: Multilingual Evaluation of Generative AI (2023.emnlp-main)
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Kabir Ahuja, Harshita Diddee, Rishav Hada, Millicent Ochieng, Krithika Ramesh, Prachi Jain, Akshay Nambi, Tanuja Ganu, Sameer Segal, Mohamed Ahmed, Kalika Bali, Sunayana Sitaram
| Challenge: | Large Large Models (LLMs) have shown impressive performance on many natural language processing tasks such as language understanding, reasoning, and language generation. |
| Approach: | They present a framework for evaluating generative LLMs in the multilingual setting and provide directions for future progress in the field. |
| Outcome: | The proposed framework evaluates generative models on 16 NLP datasets across 70 typologically diverse languages and compares them to state-of-the-art non-autoregressive models. |
On Evaluating and Mitigating Gender Biases in Multilingual Settings (2023.findings-acl)
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| Challenge: | Existing benchmarks and resources for evaluating gender biases in multilingual settings are limited. |
| Approach: | They propose to extend DisCo to different Indian languages using human annotations to evaluate gender biases in multilingual models. |
| Outcome: | The proposed benchmarks and mitigation techniques are extended beyond English to evaluate gender biases in multilingual models. |
Bridging the Language Gap: Dynamic Learning Strategies for Improving Multilingual Performance in LLMs (2025.coling-main)
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Somnath Kumar, Vaibhav Balloli, Mercy Ranjit, Kabir Ahuja, Sunayana Sitaram, Kalika Bali, Tanuja Ganu, Akshay Nambi
| Challenge: | Large language models (LLMs) excel in diverse applications but still struggle with non-Latin scripts and low-resource languages. |
| Approach: | They propose a dynamic learning approach that optimizes prompt strategy, embedding model, and LLM per query at runtime. |
| Outcome: | The proposed approach achieves 10-15% improvements in multilingual performance over pre-trained models and 4x gains compared to fine-tuned, language-specific models. |
On the Practical Ability of Recurrent Neural Networks to Recognize Hierarchical Languages (2020.coling-main)
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| Challenge: | recurrent models have been effective in NLP tasks but performance on context-free languages (CFLs) is weak. |
| Approach: | They evaluate the performance of recurrent models on Dyck-n languages . they find that they are expressive enough to recognize Dyck words of arbitrary lengths if their depths are bounded. |
| Outcome: | The proposed models generalize well on Dyck-n languages, while performing poorly on longer test strings. |
Everything you need to know about Multilingual LLMs: Towards fair, performant and reliable models for languages of the world (2023.acl-tutorials)
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| Challenge: | Responsible AI issues such as fairness, bias and toxicity will be discussed in this tutorial . |
| Approach: | This tutorial will describe various aspects of scaling up language technologies to many of the world’s languages by describing the latest research in Massively Multilingual Language Models (MMLMs). |
| Outcome: | This tutorial will cover various aspects of scaling up language technologies to many of the world's languages by describing the latest research in multilingual models. |