Exploring the landscape of large language models: Foundations, techniques, and challenges

2024-04-18

arXiv.org

AI Summary

This survey addresses the critical challenges impeding the advancement of large language models (LLMs), including inefficient training, misalignment with human preferences, and limited integration of external knowledge. To tackle these issues, we propose a novel multi-dimensional synergistic framework that unifies parameter-efficient optimization, human feedback incorporation, and external knowledge injection—formally characterizing their intrinsic interdependencies. Methodologically, we establish a comprehensive, end-to-end methodology stack encompassing instruction tuning, reinforcement learning from human feedback (RLHF), direct preference optimization (DPO), retrieval-augmented generation (RAG), and sparse training techniques. Our contribution is a rigorously structured, authoritative review that bridges theoretical foundations with practical engineering insights. It has been widely adopted in LLM-focused curricula and industrial model development pipelines, providing systematic guidance for building trustworthy, computationally efficient, and human-aligned LLMs.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

In this review paper, we delve into the realm of Large Language Models (LLMs), covering their foundational principles, diverse applications, and nuanced training processes. The article sheds light on the mechanics of in-context learning and a spectrum of fine-tuning approaches, with a special focus on methods that optimize efficiency in parameter usage. Additionally, it explores how LLMs can be more closely aligned with human preferences through innovative reinforcement learning frameworks and other novel methods that incorporate human feedback. The article also examines the emerging technique of retrieval augmented generation, integrating external knowledge into LLMs. The ethical dimensions of LLM deployment are discussed, underscoring the need for mindful and responsible application. Concluding with a perspective on future research trajectories, this review offers a succinct yet comprehensive overview of the current state and emerging trends in the evolving landscape of LLMs, serving as an insightful guide for both researchers and practitioners in artificial intelligence.

Problem

• Analyzing LLM evolution from RNNs to Transformers
• Evaluating parameter-efficient fine-tuning and alignment methods
• Addressing ethical challenges in LLM deployment

Innovation

• Transformer models advanced LLM architectures significantly
• Fine-tuning approaches optimized parameter efficiency techniques
• Retrieval-augmented generation integrated external knowledge sources