Fine-Tuning, Task-specific tuning, Instruction tuning, Continual pre-training, RLHF, PFT | Telugu

Описание: The provided sources offer a comprehensive overview of advanced techniques for adapting Large Language Models (LLMs) to specific domains, focusing heavily on *Parameter-Efficient Fine-Tuning (PEFT)**, **instruction tuning**, and **continuous pre-training**. One study specifically investigates four methods—continuous pre-training, instruct fine-tuning, NEFTune, and prompt engineering—for enhancing LLMs in **clinical applications**, finding that continuous pre-training provides a necessary foundation for subsequent instruction tuning. Another source explores the ambiguity between the terms **fine-tuning* and *continual pre-training**, clarifying that the difference often lies merely in terminology or the scale and domain specificity of the training data. Furthermore, an extensive survey breaks down various PEFT algorithms, such as **LoRA* and *Adapters**, and discusses their application across different domains like finance and vision, while a final source details the three-stage **Instruction Tuning paradigm* (task-specific, multi-task, and zero-shot) for improving open-source LLMs in the **financial sector**.