Papers by Diganta Misra
Using Shapley interactions to understand how models use structure (2025.acl-long)
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| Challenge: | Language models are intricately structured systems, and attribution measures are important for understanding how they combine features to influence outputs. |
| Approach: | They use Shapley Taylor interaction indices to examine how language and speech models internally relate and structure their inputs. |
| Outcome: | The proposed methods show that language models encode phonetic interactions . they show that the inputs are more entangled for pairs where a consonant influences a vowel or approximant . |
(Almost) Free Modality Stitching of Foundation Models (2025.emnlp-main)
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| Challenge: | Multi-modal foundation models often use modality-specific (uni-modal) models as sub-components, which are stitched together via a connector module. |
| Approach: | They propose a framework that allows for optimal uni-modal model selection and connector training by leveraging hypernetworks. |
| Outcome: | The proposed framework reduces the cost of searching for the best performing uni-modal model pair by 10 while matching the ranking and trained connector performance across diverse multi-modal benchmarks. |
Aurora-M: Open Source Continual Pre-training for Multilingual Language and Code (2025.coling-industry)
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Taishi Nakamura, Mayank Mishra, Simone Tedeschi, Yekun Chai, Jason T. Stillerman, Felix Friedrich, Prateek Yadav, Tanmay Laud, Vu Minh Chien, Terry Yue Zhuo, Diganta Misra, Ben Bogin, Xuan-Son Vu, Marzena Karpinska, Arnav Varma Dantuluri, Wojciech Kusa, Tommaso Furlanello, Rio Yokota, Niklas Muennighoff, Suhas Pai, Tosin Adewumi, Veronika Laippala, Xiaozhe Yao, Adalberto Barbosa Junior, Aleksandr Drozd, Jordan Clive, Kshitij Gupta, Liangyu Chen, Qi Sun, Ken Tsui, Nour Moustafa-Fahmy, Nicolo Monti, Tai Dang, Ziyang Luo, Tien-Tung Bui, Roberto Navigli, Virendra Mehta, Matthew Blumberg, Victor May, Hiep Nguyen, Sampo Pyysalo
| Challenge: | Pretrained language models are integral part of AI applications, but their high computational cost limits accessibility. |
| Approach: | They evaluate Aurora-M, a 15B parameter multilingual open-source model trained on English, Finnish, Hindi, Japanese, Vietnamese, and code. |
| Outcome: | The proposed model outperforms existing models on English, Finnish, Hindi, Japanese, Vietnamese, and code. |
GitChameleon 2.0: Evaluating AI Code Generation Against Python Library Version Incompatibilities (2026.acl-long)
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Diganta Misra, Nizar Islah, Victor May, Brice Rauby, Zihan Wang, Justine Gehring, Antonio Orvieto, Muawiz Sajjad Chaudhary, Eilif B. Muller, Irina Rish, Samira Ebrahimi Kahou, Massimo Caccia
| Challenge: | Existing code evolution benchmarks lack execution-based evaluation for generating code compliant with specific library versions. |
| Approach: | They propose a new Python code completion problem that evaluates the ability of large language models to perform version-conditioned code generation. |
| Outcome: | The proposed benchmarks show that state-of-the-art systems can perform version-conditioned code generation with high success rates. |