Papers by Pradeepika Verma

3 papers
M3Retrieve: Benchmarking Multimodal Retrieval for Medicine (2025.emnlp-main)

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Challenge: Strong retrieval models are increasingly important in knowledge-intensive domains.
Approach: They propose a benchmark to evaluate multimodal retrieval models in medical settings . they examine 1.2 million text documents and 164K multimodal queries .
Outcome: The proposed model spans 5 domains,16 medical fields, and 4 distinct tasks with over 1.2 Million text documents and 164K multimodal queries.
Domain Aligned Prefix Averaging for Domain Generalization in Abstractive Summarization (2023.findings-acl)

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Challenge: Existing studies on domain generalization have sophisticated training algorithms.
Approach: They propose a lightweight, weight averaging approach to domain generalization for abstractive summarization using prefix tuning and weight adjusting.
Outcome: The proposed method performs better on four diverse summarization domains compared to baselines.
MENDER: Multi-hop Commonsense and Domain-specific CoT Reasoning for Knowledge-grounded Empathetic Counseling of Crime Victims (2025.naacl-srw)

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Challenge: Experimental evaluations on counseling dialogue dataset, POEM validate MENDER’s efficacy in generating coherent, knowledge-grounded responses.
Approach: They propose a multi-hop commonsensE and domaiN-specific Chain-of-Thought reasoning framework that integrates commonsense and domain knowledge via multi-hopping reasoning over the dialogue context.
Outcome: Experimental evaluations on counseling dialogue dataset validate MENDER’s efficacy in generating coherent, empathetic, knowledge-grounded responses.

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