Papers by Poulami Ghosh
How Good Are LLMs at Processing Tool Outputs? (2026.eacl-long)
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Kiran Kate, Yara Rizk, Poulami Ghosh, Ashu Gulati, Tathagata Chakraborti, Zidane Wright, Mayank Agarwal
| Challenge: | Real-world task automation tasks require large language models to call tools, which often return complex JSON responses. |
| Approach: | They evaluated 15 open and closed weight models using multiple prompting approaches to evaluate their tool response processing task and their ability to process structured (JSON) responses. |
| Outcome: | The proposed model can process structured (JSON) responses with 3% to 50% performance differences. |
Are Language Models Agnostic to Linguistically Grounded Perturbations? A Case Study of Indic Languages (2025.findings-naacl)
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| Challenge: | Existing studies do not focus on linguistically grounded attacks, but pre-trained models are susceptible to these perturbations. |
| Approach: | They propose to examine whether pre-trained language models are agnostic to linguistically grounded attacks . they find that PLMs are less susceptible to linguistic perturbations than non-linguistic ones . |
| Outcome: | The proposed model is agnostic to linguistically grounded attacks, but is less susceptible to linguist attacks than non-linguistic models. |
A Morphology-Based Investigation of Positional Encodings (2024.emnlp-main)
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| Challenge: | Contemporary deep learning models handle languages with diverse morphology . morphological complexity of languages is closely linked with positional encodings . |
| Approach: | They propose to use positional encodings to integrate morphological complexity into deep learning models. |
| Outcome: | The proposed model improves on 22 languages and 5 downstream tasks. |