The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities
- URL: http://arxiv.org/abs/2408.13296v3
- Date: Wed, 30 Oct 2024 01:04:15 GMT
- Title: The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities
- Authors: Venkatesh Balavadhani Parthasarathy, Ahtsham Zafar, Aafaq Khan, Arsalan Shahid,
- Abstract summary: This report examines the fine-tuning of Large Language Models (LLMs)
It outlines the historical evolution of LLMs from traditional Natural Language Processing (NLP) models to their pivotal role in AI.
The report introduces a structured seven-stage pipeline for fine-tuning LLMs.
- Score: 0.35998666903987897
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This report examines the fine-tuning of Large Language Models (LLMs), integrating theoretical insights with practical applications. It outlines the historical evolution of LLMs from traditional Natural Language Processing (NLP) models to their pivotal role in AI. A comparison of fine-tuning methodologies, including supervised, unsupervised, and instruction-based approaches, highlights their applicability to different tasks. The report introduces a structured seven-stage pipeline for fine-tuning LLMs, spanning data preparation, model initialization, hyperparameter tuning, and model deployment. Emphasis is placed on managing imbalanced datasets and optimization techniques. Parameter-efficient methods like Low-Rank Adaptation (LoRA) and Half Fine-Tuning are explored for balancing computational efficiency with performance. Advanced techniques such as memory fine-tuning, Mixture of Experts (MoE), and Mixture of Agents (MoA) are discussed for leveraging specialized networks and multi-agent collaboration. The report also examines novel approaches like Proximal Policy Optimization (PPO) and Direct Preference Optimization (DPO), which align LLMs with human preferences, alongside pruning and routing optimizations to improve efficiency. Further sections cover validation frameworks, post-deployment monitoring, and inference optimization, with attention to deploying LLMs on distributed and cloud-based platforms. Emerging areas such as multimodal LLMs, fine-tuning for audio and speech, and challenges related to scalability, privacy, and accountability are also addressed. This report offers actionable insights for researchers and practitioners navigating LLM fine-tuning in an evolving landscape.
Related papers
- LLM-Powered Preference Elicitation in Combinatorial Assignment [17.367432304040662]
We study the potential of large language models (LLMs) as proxies for humans to simplify preference elicitation (PE) in assignment.
We propose a framework for LLM proxies that can work in tandem with SOTA ML-powered preference elicitation schemes.
We experimentally evaluate the efficiency of LLM proxies against human queries in the well-studied course allocation domain.
arXiv Detail & Related papers (2025-02-14T17:12:20Z) - LLMs are Also Effective Embedding Models: An In-depth Overview [40.53941563464671]
Large language models (LLMs) have revolutionized natural language processing by achieving state-of-the-art performance across various tasks.
Recently, their effectiveness as embedding models has gained attention, marking a paradigm shift from traditional encoder-only models like ELMo and BERT to decoder-only, large-scale LLMs like GPT, LLaMA, and Mistral.
arXiv Detail & Related papers (2024-12-17T06:48:24Z) - Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System [75.25394449773052]
Large Language Model (LLM) based multi-agent systems (MAS) show remarkable potential in collaborative problem-solving.
Yet they still face critical challenges: low communication efficiency, poor scalability, and a lack of effective parameter-updating optimization methods.
We present Optima, a novel framework that addresses these issues by significantly enhancing both communication efficiency and task effectiveness.
arXiv Detail & Related papers (2024-10-10T17:00:06Z) - EVOLvE: Evaluating and Optimizing LLMs For Exploration [76.66831821738927]
Large language models (LLMs) remain under-studied in scenarios requiring optimal decision-making under uncertainty.
We measure LLMs' (in)ability to make optimal decisions in bandits, a state-less reinforcement learning setting relevant to many applications.
Motivated by the existence of optimal exploration algorithms, we propose efficient ways to integrate this algorithmic knowledge into LLMs.
arXiv Detail & Related papers (2024-10-08T17:54:03Z) - Achieving Peak Performance for Large Language Models: A Systematic Review [0.0]
Large language models (LLMs) have achieved remarkable success in natural language processing (NLP)
As models grow into the trillion- parameter range, computational and memory costs increase significantly.
This makes it difficult for many researchers to access the resources needed to train or apply these models.
arXiv Detail & Related papers (2024-09-07T13:57:41Z) - One Token Can Help! Learning Scalable and Pluggable Virtual Tokens for Retrieval-Augmented Large Language Models [67.49462724595445]
Retrieval-augmented generation (RAG) is a promising way to improve large language models (LLMs)
We propose a novel method that involves learning scalable and pluggable virtual tokens for RAG.
arXiv Detail & Related papers (2024-05-30T03:44:54Z) - ORLM: A Customizable Framework in Training Large Models for Automated Optimization Modeling [15.67321902882617]
We introduce OR-Instruct, a semi-automated data synthesis framework for optimization modeling.
We also introduce IndustryOR, the first industrial benchmark for evaluating LLMs in solving practical OR problems.
arXiv Detail & Related papers (2024-05-28T01:55:35Z) - Multi-Reference Preference Optimization for Large Language Models [56.84730239046117]
We introduce a novel closed-form formulation for direct preference optimization using multiple reference models.
The resulting algorithm, Multi-Reference Preference Optimization (MRPO), leverages broader prior knowledge from diverse reference models.
Our experiments demonstrate that LLMs finetuned with MRPO generalize better in various preference data, regardless of data scarcity or abundance.
arXiv Detail & Related papers (2024-05-26T00:29:04Z) - Large Language Model-Based Evolutionary Optimizer: Reasoning with
elitism [1.1463861912335864]
Large Language Models (LLMs) have demonstrated remarkable reasoning abilities.
This paper asserts that LLMs possess the capability for zero-shot optimization across diverse scenarios.
We introduce a novel population-based method for numerical optimization using LLMs.
arXiv Detail & Related papers (2024-03-04T13:57:37Z) - How Can LLM Guide RL? A Value-Based Approach [68.55316627400683]
Reinforcement learning (RL) has become the de facto standard practice for sequential decision-making problems by improving future acting policies with feedback.
Recent developments in large language models (LLMs) have showcased impressive capabilities in language understanding and generation, yet they fall short in exploration and self-improvement capabilities.
We develop an algorithm named LINVIT that incorporates LLM guidance as a regularization factor in value-based RL, leading to significant reductions in the amount of data needed for learning.
arXiv Detail & Related papers (2024-02-25T20:07:13Z) - FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large
Language Models in Federated Learning [70.38817963253034]
This paper first discusses these challenges of federated fine-tuning LLMs, and introduces our package FS-LLM as a main contribution.
We provide comprehensive federated parameter-efficient fine-tuning algorithm implementations and versatile programming interfaces for future extension in FL scenarios.
We conduct extensive experiments to validate the effectiveness of FS-LLM and benchmark advanced LLMs with state-of-the-art parameter-efficient fine-tuning algorithms in FL settings.
arXiv Detail & Related papers (2023-09-01T09:40:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.