Harshita Sharma


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Exploring the Boundaries of GPT-4 in Radiology
Qianchu Liu | Stephanie Hyland | Shruthi Bannur | Kenza Bouzid | Daniel Castro | Maria Wetscherek | Robert Tinn | Harshita Sharma | Fernando Pérez-García | Anton Schwaighofer | Pranav Rajpurkar | Sameer Khanna | Hoifung Poon | Naoto Usuyama | Anja Thieme | Aditya Nori | Matthew Lungren | Ozan Oktay | Javier Alvarez-Valle
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

The recent success of general-domain large language models (LLMs) has significantly changed the natural language processing paradigm towards a unified foundation model across domains and applications. In this paper, we focus on assessing the performance of GPT-4, the most capable LLM so far, on the text-based applications for radiology reports, comparing against state-of-the-art (SOTA) radiology-specific models. Exploring various prompting strategies, we evaluated GPT-4 on a diverse range of common radiology tasks and we found GPT-4 either outperforms or is on par with current SOTA radiology models. With zero-shot prompting, GPT-4 already obtains substantial gains ( 10% absolute improvement) over radiology models in temporal sentence similarity classification (accuracy) and natural language inference (F1). For tasks that require learning dataset-specific style or schema (e.g. findings summarisation), GPT-4 improves with example-based prompting and matches supervised SOTA. Our extensive error analysis with a board-certified radiologist shows GPT-4 has a sufficient level of radiology knowledge with only occasional errors in complex context that require nuanced domain knowledge. For findings summarisation, GPT-4 outputs are found to be overall comparable with existing manually-written impressions.


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HAWP: a Dataset for Hindi Arithmetic Word Problem Solving
Harshita Sharma | Pruthwik Mishra | Dipti Sharma
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Word Problem Solving remains a challenging and interesting task in NLP. A lot of research has been carried out to solve different genres of word problems with various complexity levels in recent years. However, most of the publicly available datasets and work has been carried out for English. Recently there has been a surge in this area of word problem solving in Chinese with the creation of large benchmark datastes. Apart from these two languages, labeled benchmark datasets for low resource languages are very scarce. This is the first attempt to address this issue for any Indian Language, especially Hindi. In this paper, we present HAWP (Hindi Arithmetic Word Problems), a dataset consisting of 2336 arithmetic word problems in Hindi. We also developed baseline systems for solving these word problems. We also propose a new evaluation technique for word problem solvers taking equation equivalence into account.