Arjun Rajaram

Anthony Olakangil, Cindy Wang, Justin Nguyen et al.

After the COVID-19 pandemic caused internet usage to grow by 70%, there has been an increased number of people all across the world using social media. Applications like Twitter, Meta Threads, YouTube, and Reddit have become increasingly pervasive, leaving almost no digital space where public opinion is not expressed. This paper investigates sentiment and semantic relationships among comments across various social media platforms, as well as discusses the importance of shared opinions across these different media platforms, using word embeddings to analyze components in sentences and documents. It allows researchers, politicians, and business representatives to trace a path of shared sentiment among users across the world. This research paper presents multiple approaches that measure the relatedness of text extracted from user comments on these popular online platforms. By leveraging embeddings, which capture semantic relationships between words and help analyze sentiments across the web, we can uncover connections regarding public opinion as a whole. The study utilizes pre-existing datasets from YouTube, Reddit, Twitter, and more. We made use of popular natural language processing models like Bidirectional Encoder Representations from Transformers (BERT) to analyze sentiments and explore relationships between comment embeddings. Additionally, we aim to utilize clustering and Kl-divergence to find semantic relationships within these comment embeddings across various social media platforms. Our analysis will enable a deeper understanding of the interconnectedness of online comments and will investigate the notion of the internet functioning as a large interconnected brain.

Daniel Nichols, Joshua H. Davis, Zhaojun Xie et al.

Large language models are increasingly becoming a popular tool for software development. Their ability to model and generate source code has been demonstrated in a variety of contexts, including code completion, summarization, translation, and lookup. However, they often struggle to generate code for complex programs. In this paper, we study the capabilities of state-of-the-art language models to generate parallel code. In order to evaluate language models, we create a benchmark, ParEval, consisting of prompts that represent 420 different coding tasks related to scientific and parallel computing. We use ParEval to evaluate the effectiveness of several state-of-the-art open- and closed-source language models on these tasks. We introduce novel metrics for evaluating the performance of generated code, and use them to explore how well each large language model performs for 12 different computational problem types and six different parallel programming models.

Manit Mishra, Abderrahman Braham, Charles Marsom et al.

Conventional processes for analyzing datasets and extracting meaningful information are often time-consuming and laborious. Previous work has identified manual, repetitive coding and data collection as major obstacles that hinder data scientists from undertaking more nuanced labor and high-level projects. To combat this, we evaluated OpenAI's GPT-3.5 as a "Language Data Scientist" (LDS) that can extrapolate key findings, including correlations and basic information, from a given dataset. The model was tested on a diverse set of benchmark datasets to evaluate its performance across multiple standards, including data science code-generation based tasks involving libraries such as NumPy, Pandas, Scikit-Learn, and TensorFlow, and was broadly successful in correctly answering a given data science query related to the benchmark dataset. The LDS used various novel prompt engineering techniques to effectively answer a given question, including Chain-of-Thought reinforcement and SayCan prompt engineering. Our findings demonstrate great potential for leveraging Large Language Models for low-level, zero-shot data analysis.