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/2206.07682https://arxiv.org/pdf/2503.05788