Core Concepts
Fine-tuned models outperform GPT 3.5 Turbo, while RAG approach excels further with system prompting.
Abstract
The content explores the comparison between fine-tuning, retrieval-augmented generation (RAG), and system prompting for large language models (LLMs). It focuses on establishing performance baselines for non-specialist users by testing GPT 3.5 in different configurations. The study reveals that fine-tuned models outperform the unmodified version, with RAG showing even better results, especially when combined with a system prompt. The methodology involved creating datasets related to LayerZero cryptocurrency project and testing responses to various questions.
Abstract:
Research focuses on improving LLMs through fine-tuning, RAG, and system prompting.
Testing GPT 3.5 unmodified, fine-tuned versions, and RAG database access with system prompts.
Commercial platforms used to establish baseline outputs for non-expert users.
Introduction:
Academic research on improving base LLMs through fine-tuning and RAG discussed.
Fine-tuning process detailed along with task-specific objectives.
RAG explained as passing information via multi-stage retrieval system.
Existing Literature:
Studies on applications of GPT models in financial text analysis tasks discussed.
Efforts to improve model accuracy through methods like fine-tuning explored.
Technical Background:
Comparison between fine-tuning settings using OpenAI's API and RAG framework by KIPLEY.AI detailed.
Knowledge Base Creator Module explained for data integration into knowledge bases.
Methodology and Data:
Dataset preparation focused on LayerZero cryptocurrency project post September 2021.
Testing conducted with sets of questions using unmodified GPT 3.5 Turbo, fine-tuned model, and augmented model.
Results:
RAG outperformed fine-tuning which was better than the unmodified model.
System prompts improved accuracy significantly across all models tested.
Analysis:
Unmodified model showed ability to guess answers accurately despite lack of information.
Fine-tuned model exhibited more inaccuracies possibly due to reinforcement learning hindrance.
Conclusion:
RAG proved more effective than fine-tuning for LLM improvement in this study.
Recommendations made for commercial users based on performance outcomes.
Stats
"if commercial platforms are used and default settings are applied with no iteration in order to establish a baseline set of outputs"
"a set of 100 relevant questions relating primarily to events that occurred after September 2021"
"the basic un-fine-tuned gpt-3.5 turbo model augmented via access to the pkl vector database"