The Double-Edged Sword of Fine-Tuning LLMs: Balancing Accuracy and Interpretability
Fine-tuning Large Language Models (LLMs) has revolutionized the field of Natural Language Processing (NLP) by significantly improving model accuracy and adaptability. However, beneath the surface of these finely-tuned LLMs lies a double-edged sword – a delicate trade-off between accuracy and interpretability.
On one hand, fine-tuning enables LLMs to learn from vast amounts of data, resulting in unparalleled accuracy and contextual understanding. But, on the other hand, this process often comes at the cost of transparency and explainability. Fine-tuning can mask biases and uncertainties within the model, making it challenging to identify and address these issues.
The Risks of Interpretability
When LLMs prioritize accuracy, they may:
- Oversimplify complex relationships: By focusing on high-accuracy predictions, models may overlook intricate relationships between variables, leading to...
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