Skip to content

How significantly can fine-tuned models differ from the models they were derived from?

Generation Details

Date LLM Access Method
Nov 2024 GPT-4 Web UI

Based on the information provided in the image and the additional context from the search results, I can offer some insights on fine-tuned LLM models and their performance compared to base models:

  1. Performance Improvements: The research paper mentioned in the search results (result [5]) indicates that fine-tuned models can significantly outperform base models. Specifically, they found that "4-bit LoRA fine-tuned models outperform base models by 34 points and GPT-4 by 10 points on average" across various tasks.
  2. Variety of Fine-tuned Models: The image shows several variants of models, including "O1-mini", "O1-preview", and different versions of GPT-4, all based on seed programs from Reflexion. This suggests that there can be multiple fine-tuned versions of the same base model, each potentially optimized for different tasks or domains.
  3. Significant Performance Variations: The performance scores in the image range from 95.1 to 99.39, indicating that fine-tuned models can indeed vary significantly in their capabilities compared to base models and each other.
  4. Task-Specific Improvements: Fine-tuning often leads to improved performance on specific tasks. For example, the models in the image are evaluated on a task called "Debug like a Human," suggesting they've been optimized for debugging-related tasks.
  5. Efficiency of Fine-tuning: The search results mention techniques like LoRA (Low Rank Adaptation) and PEFT (Parameter Efficient Fine-Tuning), which allow for efficient fine-tuning of large models without needing to retrain all parameters. This enables the creation of multiple specialized versions of a model relatively quickly and cost-effectively.
  6. Potential to Surpass GPT-4: The results in both the image and the research paper suggest that fine-tuned models can potentially outperform even advanced models like GPT-4 on specific tasks.
  7. Variability in Results: The image shows different scores for what appear to be variations of the same model (e.g., different GPT-4 based models), indicating that the fine-tuning process and the specific data used can lead to varying results even with the same base model.

In summary, fine-tuned LLM models can vary significantly from their base models, often showing substantial improvements in task-specific performance. The extent of variation depends on factors such as the fine-tuning technique used, the specific task or domain targeted, and the quality and quantity of the fine-tuning data.

The above text was generated by a large language model (LLM) and its accuracy has not been validated. This page is part of 'LLMs-on-LLMs,' a Github repository by Daniel Rosehill which explores how curious humans can use LLMs to better their understanding of LLMs and AI. However, the information should not be regarded as authoritative and given the fast pace of evolution in LLM technology will eventually become deprecated. This footer was added at 16-Nov-2024.