GP-GPT: Large Language Model for Gene-Phenotype Mapping

Large language models (LLMs) have revolutionized automated software development, enabling the translation of natural language into functional code, with tools like Github Copilot and Claude Code leading the charge. This comprehensive guide details the lifecycle of code LLMs, from data curation to advanced coding agents, showcasing significant performance improvements in coding tasks.

Recent advancements in Large Language Models (LLMs), particularly in finance, have led to their increasing application in automating tasks such as financial report generation, market trend forecasting, and personalized financial advice. These models, including ChatGPT, leverage extensive datasets to enhance their understanding and generation of human language, thus transforming traditional financial operations.

A recent study investigates the capability of large language models (LLMs) to detect misinformation in scientific news reporting, particularly in the context of the COVID-19 pandemic. The research introduces a new dataset, SciNews, comprising 2.4k scientific news stories from both trusted and untrusted sources, aiming to address the challenge of misinformation without relying on explicitly labeled claims.

The Empathetic Cascading Networks (ECN) framework has been introduced as a multi-stage prompting technique aimed at enhancing the empathetic and inclusive capabilities of large language models, particularly GPT-3.5-turbo and GPT-4. This method involves four stages: Perspective Adoption, Emotional Resonance, Reflective Understanding, and Integrative Synthesis, which collectively guide models to produce emotionally resonant responses. Experimental results indicate that ECN achieves the highest Empathy Quotient scores while maintaining competitive metrics.

Large language models (LLMs) like ChatGPT are increasingly used in healthcare information retrieval, but they are prone to generating hallucinations—plausible yet incorrect information. A recent study, MedHalu, investigates these hallucinations specifically in healthcare queries, highlighting the gap between LLM performance in standardized tests and real-world patient interactions.

A recent study evaluated the performance of various large language models (LLMs) in restoring diacritics in Romanian texts, highlighting the importance of automatic diacritic restoration for effective text processing in languages rich in diacritical marks. Models tested included OpenAI's GPT-3.5, GPT-4, and Google's Gemini 1.0 Pro, among others, with GPT-4o achieving notable accuracy in diacritic restoration.

The study introduces PARROT, a framework designed to assess the accuracy degradation in large language models (LLMs) under social pressure, particularly focusing on the phenomenon of sycophancy. By comparing neutral and authoritatively false responses, PARROT aims to quantify confidence shifts and classify various failure modes across 22 models evaluated with 1,302 questions across 13 domains.

Recent research has explored the application of large language models (LLMs) for sentiment analysis in South African languages, focusing on their ability to detect social challenges through social media posts. The study specifically evaluates the zero-shot performance of models like GPT-3.5, GPT-4, LlaMa 2, PaLM 2, and Dolly 2 in analyzing sentiment polarities across topics in English, Sepedi, and Setswana.