Papers by Anwesan Pal
Tagging-Augmented Generation: Assisting Language Models in Finding Intricate Knowledge In Long Contexts (2025.emnlp-industry)
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Anwesan Pal, Karen Hovsepian, Tinghao Guo, Mengnan Zhao, Somendra Tripathi, Nikos Kanakaris, George Mihaila, Sumit Nigam
| Challenge: | Recent studies into effective context lengths of flagship large language models (LLMs) have revealed major limitations in effective question answering (QA) and reasoning over long and complex contexts for even the largest and most impressive cadre of models. |
| Approach: | They propose a lightweight data augmentation strategy that boosts LLM performance in long-context scenarios without degrading and altering the integrity and composition of retrieved documents. |
| Outcome: | The proposed strategy boosts performance in long-context scenarios without degrading and altering the integrity and composition of retrieved documents. |
ELLA: Efficient Lifelong Learning for Adapters in Large Language Models (2026.eacl-long)
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| Challenge: | Existing approaches to training Large Language Models (LLMs) suffer from catastrophic forgetting when adapted sequentially to new tasks in a continual learning (CL) setting. Existing methods are impractical and could potentially violate privacy. |
| Approach: | They propose a training framework built on the principle of selective subspace de-correlation that characterizes the structure of past updates and penalizes alignments along their high-energy, task-specific directions. |
| Outcome: | The proposed training framework achieves state-of-the-art CL performance on three popular benchmarks spanning both classification and generative tasks with relative accuracy gains of up to 9.6% and a 35 smaller memory footprint. |