Exploring Advanced Large Language Models with LLMsuite

Authors: Giorgio Roffo

Keywords: Language Model Benchmarking, Pre-Trained LLM Comparison, LLM Performance Analysis, NLP Model Evaluation Tools, Public Dataset Inference for LLMs, BLEU and ROUGE Metrics for LLM, Open Source LLM Testing Tools, Large Language Model Evaluation Software, NLP Benchmarking Suite, Comprehensive LLM Evaluation Toolkit
License: CC BY 4.0

Abstract: This tutorial explores the advancements and challenges in the development of Large Language Models (LLMs) such as ChatGPT and Gemini. It addresses inherent limitations like temporal knowledge cutoffs, mathematical inaccuracies, and the generation of incorrect information, proposing solutions like Retrieval Augmented Generation (RAG), Program-Aided Language Models (PAL), and frameworks such as ReAct and LangChain. The integration of these techniques enhances LLM performance and reliability, especially in multi-step reasoning and complex task execution. The paper also covers fine-tuning strategies, including instruction fine-tuning, parameter-efficient methods like LoRA, and Reinforcement Learning from Human Feedback (RLHF) as well as Reinforced Self-Training (ReST). Additionally, it provides a comprehensive survey of transformer architectures and training techniques for LLMs. The toolbox for implementing these techniques is publicly available at https://github.com/giorgioroffo/large_language_models_open_suite

Submitted to arXiv on 01 Jul. 2024

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