This repository serves as a platform for testing, evaluating, and exploring the capabilities of state-of-the-art large language models (LLMs). We aim to delve into the fundamental aspects of these powerful models, understanding their strengths, weaknesses, and potential applications.
The primary objectives of this endeavor are:
-Benchmarking LLM Performance: We will compare and contrast the performance of various LLM architectures across a range of tasks, including text generation, translation, question answering, and code summarization.
-Evaluating LLM Biases: We will investigate the inherent biases and biases introduced by training data in LLMs. This includes identifying potential biases in factual outputs and understanding their impact on downstream applications.
-Exploring LLM Applications: We will explore the diverse applications of LLMs, venturing into areas like creative writing, natural language understanding, and code generation.