A report dedicated to the most current research aimed at using Large Language Models (LLMs) in the field of Sentiment Analysis. This task involves extracting the author’s opinion from the text to the entity mentioned in the text. You will learn:(1) how well LLMs reasoning can be used for reasoning in sentiment analysis as in Zero-shot-Learning,(2) how to improve reasoning by applying and leaving step-by-step chains (Chain-of- Thought),(3) how to prepare the most advanced model in sentiment analysis using Chain-of-Thought. The report is expected to provide results and conclusions based on experiments on the RuSentNE-2023 corpus for open models and proprietary (ChatGPT) for reasoning in the field of sentiment analysis on English and non-English texts
A report dedicated to the most current research aimed at using Large Language Models (LLMs) in the field of Sentiment Analysis. This task involves extracting the author’s opinion from the...
In this speech, I will provide an overview of the current challenges faced by healthcare professionals in accessing and interpreting vast amounts of patient data. I will discuss how our...
Clinical decision making is often based on analysis of retrospective patient cohorts to enable and guide treatment decisions. In general, there are large volumes of data within hospitals within Electronic...
In today’s landscape of AI-driven recruitment, candidate-job matching models play a pivotal role in enhancing the hiring process’s efficiency and effectiveness. This necessitates rigorous evaluation to ensure fairness and equity....
Demos
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