ChatGPT, Bard, and Large Language Models for Biomedical Research: Opportunities and Pitfalls.

Large Language Models (LLMs) such as ChatGPT and Bard have emerged as groundbreaking interactive chatbots, capturing significant attention and transforming the biomedical research landscape. These powerful tools offer immense potential for advancing scientific inquiry, but they also present challenges and pitfalls. Leveraging large language models, researchers can streamline literature reviews, summarize complex findings, and even generate novel hypotheses, enabling the exploration of uncharted scientific territories. However, the inherent risk of misinformation and misleading interpretations underscores the critical importance of rigorous validation and verification processes. This article provides a comprehensive overview of the current landscape and delves into the opportunities and pitfalls associated with employing LLMs in biomedical research. Furthermore, it sheds light on strategies to enhance the utility of LLMs in biomedical research, offering recommendations to ensure their responsible and effective implementation in this domain. The findings presented in this article contribute to the advancement of biomedical engineering by harnessing the potential of LLMs while addressing their limitations.

Explainable hybrid word representations for sentiment analysis of financial news.

Due to the increasing interest of people in the stock and financial market, the sentiment analysis of news and texts related to the sector is of utmost importance. This helps the potential investors in deciding what company to invest in and what are their long-term benefits. However, it is challenging to analyze the sentiments of texts related to the financial domain, given the enormous amount of information available. The existing approaches are unable to capture complex attributes of language such as word usage, including semantics and syntax throughout the context, and polysemy in the context. Further, these approaches failed to interpret the models' predictability, which is obscure to humans. Models' interpretability to justify the predictions has remained largely unexplored and has become important to engender users' trust in the predictions by providing insight into the model prediction. Accordingly, in this paper, we present an explainable hybrid word representation that first augments the data to address the class imbalance issue and then integrates three embeddings to involve polysemy in context, semantics, and syntax in a context. We then fed our proposed word representation to a convolutional neural network (CNN) with attention to capture the sentiment. The experimental results show that our model outperforms several baselines of both classic classifiers and combinations of various word embedding models in the sentiment analysis of financial news. The experimental results also show that the proposed model outperforms several baselines of word embeddings and contextual embeddings when they are separately fed to a neural network model. Further, we show the explainability of the proposed method by presenting the visualization results to explain the reason for a prediction in the sentiment analysis of financial news.