The Predictability and Fine-Tuning of Large Language Models' Abilities
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#05539463 ver:0.05
In a recent study by researchers at Stanford University, the notion that the sudden jumps in abilities observed in large language models (LLMs) are unpredictable and emergent is challenged. The study argues that these jumps are actually a consequence of how researchers measure the LLM's performance. The researchers found that as the number of parameters in the LLMs increased, their ability to perform tasks such as addition improved gradually and predictably, rather than undergoing a sudden jump. This suggests that the abilities of LLMs are more predictable than previously thought, and the choice of metric used to measure their performance plays a significant role .
However, other scientists argue that the work does not fully dispel the notion of emergence and that the unpredictability of ...
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