Analysis
This article dives deep into the mathematical underpinnings of Generative AI, exploring why semantic drift, the tendency of Large Language Models (LLMs) to stray from their intended meaning, is so persistent. It highlights the inherent limitations of techniques like Fine-tuning in completely eradicating this phenomenon, attributing it to the probabilistic nature of the Softmax function.
Key Takeaways
- •Semantic drift, the tendency of LLMs to lose their original meaning, is not simply a flaw in prompting or history management but a fundamental characteristic of the Softmax function.
- •Fine-tuning can shift the probability distribution but cannot eliminate the risk of the model selecting incorrect tokens due to the inherent randomness of the sampling process.
- •The article uses information theory to quantify drift, treating it as the divergence between the ideal response and the model's actual output.
Reference / Citation
View Original"The article explains that Fine-tuning only shifts the distribution of logits, and as long as the temperature (T) is greater than 0, the probability of selecting incorrect tokens will mathematically never be zero. This means Generative AI's probabilistic nature leads to a non-deterministic gamble."