A Comprehensive and Critical Survey of Large Language Model Inference and Feature Generation

Recent developments in Large Language Models (LLMs) have significantly transformed natural language processing by enhancing capabilities in reasoning, decision-making, and feature representation. However, current literature often presents fragmented insights or narrowly focused evaluations. To address this, our survey provides an overview of 10 representative LLM techniques across three main categories: reasoning frameworks (e.g., Chain-of-Thought, Tree-of-Thought, RAG, RATT), feature generation mechanisms (e.g., TIFG, DAFG, TFWT), and auxiliary support strategies (e.g., Prototypical Reward Modeling, data augmentation). We systematically compare these methods across dimensions such as scalability, interpretability, and practical applicability. Furthermore, we contextualize these techniques through real-world case studies in fraud detection, education, and healthcare. This work not only synthesizes current advancements but also identifies gaps, challenges, and opportunities for future research in LLM-driven system design.