Papers by Animesh Kumar
Data Contamination Can Cross Language Barriers (2024.emnlp-main)
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| Challenge: | Existing methods to detect contamination of public benchmarks are too superficial to reflect deeper forms of contamination. |
| Approach: | They propose generalization-based approaches to unmask a cross-lingual form of contamination that inflates LLMs’ performance while evading current detection methods. |
| Outcome: | The proposed model outperforms existing detection methods while avoiding contamination of public benchmarks in the pre-training data. |
Beyond Scaling: Predicting Patent Approval with Domain-specific Fine-grained Claim Dependency Graph (2024.acl-long)
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| Challenge: | Scaling up language models has demonstrated predictable improvement and unprecedented abilities in many language tasks. |
| Approach: | They propose a fine-grained cLAim depeNdency graph that captures the dependencies within the patent data and extends the embedding-based state-of-the-art (SOTA) they then explore prompt-based methods to harness proprietary LLMs' potential, but find the best results close to random guessing, underlining the ineffectiveness of model scaling-up. |
| Outcome: | The proposed graph methods outperform the standard model scaling methods in the patent approval prediction task and show that they are cost-effective. |
Navigating the Cultural Kaleidoscope: A Hitchhiker’s Guide to Sensitivity in Large Language Models (2025.naacl-long)
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Somnath Banerjee, Sayan Layek, Hari Shrawgi, Rajarshi Mandal, Avik Halder, Shanu Kumar, Sagnik Basu, Parag Agrawal, Rima Hazra, Animesh Mukherjee
| Challenge: | Cultural harm arises when LLMs misrepresent or normalize values, identities, and practices in ways that conflict with the norms of diverse cultural groups. |
| Approach: | They propose a cultural harm test dataset and a preference dataset to assess model outputs across different cultural contexts. |
| Outcome: | The proposed model improves model behavior significantly reducing the likelihood of generating culturally insensitive or harmful content. |