Omid Rohanian — accepted AI-conference papers

2024

Large Language Models Are Poor Clinical Decision-Makers: A Comprehensive Benchmark

Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xianfeng Tang +13

EMNLP 2024main

The adoption of large language models (LLMs) to assist clinicians has attracted remarkable attention. Existing works mainly adopt the close-ended question-answering (QA) task with answer options for evaluation. However, many clinical decisions involve answering open-ended questions without pre-set o…

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2023

Disfluent Cues for Enhanced Speech Understanding in Large Language Models

Morteza Rohanian, Farhad Nooralahzadeh, Omid Rohanian, David A. Clifton, Michael Krauthammer

EMNLP 2023long findings

In computational linguistics, the common practice is to "clean" disfluent content from spontaneous speech. However, we hypothesize that these disfluencies might serve as more than mere noise, potentially acting as informative cues. We use a range of pre-trained models for a reading comprehension tas…

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