Papers by Ishan Agrawal

2 papers
CausalCite: A Causal Formulation of Paper Citations (2024.findings-acl)

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Challenge: citation counts are often criticized for failing to accurately reflect the true impact of a paper.
Approach: They propose a method to measure the impact of a paper on follow-up papers by comparing similar papers by cosine similarity.
Outcome: The proposed method is based on a new causal inference method, TextMatch.
“What’s Up, Doc?”: Analyzing How Users Seek Health Information in Large-Scale Conversational AI Datasets (2025.findings-emnlp)

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Challenge: a growing number of people are seeking healthcare information from large language models via chatbots, yet the nature and inherent risks of these interactions remain unexplored.
Approach: They use a curated dataset of 11K real-world conversations composed of 25K user messages to analyze user interactions across 21 health specialties.
Outcome: The proposed dataset consists of 11K real-world conversations composed of 25K user messages.

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