Is there really any technical content in fine-tuning large models?
This article deeply analyzes the technical content of large model fine-tuning, and combines specific data and cases to explore the impact of data quality, parameter adjustment, and experimental analysis on the fine-tuning effect.
Core content:
1. The decisive role of data quality on the fine-tuning effect, and high-tech data construction methods
2. The development of efficient parameter fine-tuning technology and the improvement of technical requirements
3. Systematic experimental verification of fine-tuning effects and key indicator analysis
At present, large model fine-tuning has become one of the focuses of much attention. However, there are different opinions in the industry as to whether large model fine-tuning has technical content and the extent of its technical content. This article will deeply explore the technical content of large model fine-tuning from multiple dimensions and combined with specific data.
1. Data quality: the first watershed of technical content
The core logic of fine-tuning is to carve model capabilities with specific data , but the quality of data directly determines success or failure:
Low-tech approach : Directly applying existing open source data (such as Alpaca format) can only generate "correct but mediocre" answers;
High-tech practice :
1. Build real-world scenario data through user log analysis (such as breaking down user questions into “outline generation + chapter continuation”), and improve the model’s task adaptability by more than 30%;
2. Introducing adversarial samples to enhance data diversity can improve the model's noise resistance by 40%;
3. Combining RLHF (human feedback reinforcement learning) to dynamically optimize data distribution, after OpenAI applied it on GPT-3, the accuracy of the model's alignment with human intentions increased by 57%.
Data proves: After Zhipu AI's GLM-4-Flash model optimized data through user interaction logs, the content coherence score in the novel creation scenario increased from 6.2 to 8.5 (out of 10).
2. Parameter adjustment: From "great effort brings about miracles" to "little effort brings about great results"
Early full parameter fine-tuning required hundreds of GB of video memory, while the current Parameter Efficient Fine-tuning (PEFT) technology only requires adjusting 0.1%-1% of the parameters to achieve similar results, but the technical requirements are higher:
LoRA technology : Rank setting needs to balance overfitting and task feature capture. Experiments show that when the rank value exceeds 256, the accuracy of the model in open domain question answering decreases by 15%;
Mixed precision training : The FP16 and FP32 switching strategy affects the convergence speed. After optimization, the training time is shortened by 30%;
Adapter module : In the GLM-4-Plus model, multi-task compatibility is achieved by inserting an adapter layer, with only a 5% loss in inference speed.
Data proves: After Baidu Wenxin's large model adopted LoRA, the fine-tuning video memory requirement was reduced from 320GB to 24GB, and the training cost was reduced by 92%.
3. Experimental analysis: the ultimate testing ground for technical content
The fine-tuning effect needs to be verified through systematic experiments. The key indicators include:
Overfitting and catastrophic forgetting : The unoptimized fine-tuned model has an accuracy of 98% in the training set, but the performance in the real scene drops sharply to 62%;
Through pre-training model capability analysis (such as continuing to write test samples), the root cause of the problem can be located, and the generalization capability is improved by 25% after adjustment.
General Ability Balance :
Fine-tuning of specific tasks may cause other capabilities to decrease by 15%-20%, while the model generality score combined with Benchmark testing can be maintained above 85%.
Data proves: In the text image task, after adversarial sample training, the image aesthetic score (AES) of Zhipu AI's CogView-3-Plus model was improved from 7.1 to 8.3.
4. Conclusion: Technical content depends on "depth of cognition"
The technical value of fine-tuning is reflected in two dimensions:
Explicit technology: quantifiable aspects such as data engineering, parameter optimization, and experimental design;
Implicit experience: non-codifiable abilities such as intuition about model behavior (such as prediction of overfitting) and transfer of domain knowledge (such as deconstructing the logic of literary creation).
Final data anchor: According to the 2025 industry report, companies that adopt high-tech fine-tuning strategies have an average user satisfaction rate of 89% after the model is launched, far exceeding the industry benchmark of 67%.
There are no shortcuts in technology, but cognition can break through bottlenecks—fine-tuning is both a science and an art.
