# kaal:claim:4755632-003

**Claim.** The move by AI developers toward smaller training datasets raises the risk of overfitting, especially with complex models, which forces LLM developers to rely on regularization to counteract overfitting of the model to the training data.

**Type.** failure  **Support.** argued

**Holds when.**

- holds for smaller datasets used in LLM development
- risk increases with model complexity

**Source quote.**

> However, with small datasets in LLMs, the risk of overfitting also rises, especially with complex models. Therefore, LLM developers have to turn to regularization in an effort to address overfitting of the model with the training data.

**From.** Wulf A. Kaal, *AI Learning - Decentralized Governance to Optimize Human Output Datasets for AI Learning* (2024), Background: Pain Point Data Quality, page 8

**Cite as.** Wulf A. Kaal, AI Learning - Decentralized Governance to Optimize Human Output Datasets for AI Learning (2024). SSRN: https://ssrn.com/abstract=4755632

**Verify.** sha256 of source PDF `972ccebf0c06ac1767a9e443bb95942b7670e806a63c25ee817c368a64c8eca8` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202024%20-%20AI%20Learning%20-%20Decentralized%20Governance%20to%20Optimize%20Human%20Output%20Datasets%20for%20AI%20Learning.pdf

**Failure mode.** Small Dataset Overfitting  (family: ai-model-and-training-failure)

**Topics.** ai-and-agents, education-and-practice

**Keywords.** overfitting, small-data, regularization, llm-training

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