Belangrijkste concepten
Fine-tuned models outperform GPT 3.5 Turbo, while RAG approach excels further with system prompting.
Samenvatting
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.
Statistieken
"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"