The sources (October 2022, March 2025) provide an extensive examination of **emergent abilities** in large language models (LLMs), defining them as unpredictable, sharp performance increases on specific tasks that occur only after models reach a critical scale. The initial source establishes this concept through empirical evidence on benchmarks like BIG-Bench, showing tasks where performance jumps suddenly from near-random, particularly in **few-shot prompting** and specialized prompting techniques like Chain-of-Thought. The subsequent survey source expands on this by framing emergence within the broader context of **in-context learning**, discussing how factors like model quantization, task complexity, and pre-training loss thresholds influence the appearance of these abilities. Both sources acknowledge the ongoing debate about whether these sudden leaps are genuine phenomena or merely **artifacts of evaluation metrics** that do not award partial credit, while also highlighting the emergence of **harmful behaviors** and advanced **reasoning capabilities** in LLM-powered AI agents as scale increases.

Sources:

https://arxiv.org/pdf/2206.07682

https://arxiv.org/pdf/2503.05788