Papers by Saptarshi Sengupta
TOP-Training: Target-Oriented Pretraining for Medical Extractive Question Answering (2025.coling-main)
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| Challenge: | e-health records underscore the growing significance of information extraction (IE) from these datasets. |
| Approach: | They propose a target-oriented pre-training paradigm for extractive question-answering in the medical domain . TOP-Training moves one step further than popular domain-oriented fine-tuning . |
| Outcome: | The proposed method improves on the Medical-EQA benchmarks. |
Exploring Language Model Generalization in Low-Resource Extractive QA (2025.coling-main)
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| Challenge: | Existing LLMs struggle with dataset demands of closed domains such as medicine and law . current LLM performance in closed domain is lacking, even on traditional tasks such as Natural Language Inference . |
| Approach: | They investigate Extractive Question Answering (EQA) with Large Language Models (LLMs) under domain drift . they find that LLMs struggle with dataset demands of closed domains . |
| Outcome: | The proposed model performs poorly in extractive question answering tasks under domain drift . the proposed model can generalize to domains that require specific knowledge without training . |
ToolDreamer: Instilling LLM Reasoning Into Tool Retrievers (2026.eacl-long)
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| Challenge: | Existing retrieval models rank tools based on similarity between query and tool description (TD) Existing tools are not conditioned to learn tool-to-tool relationships (middle). |
| Approach: | They propose a framework that conditions retrieval models to fetch tools based on hypothetical (synthetic) TD generated using an LLM. |
| Outcome: | The proposed framework improves the performance of sparse and dense retrievers with and without training, showcasing its flexibility. |