Unraveling the Nature of Emergent Abilities in Large Language Models: The Role of In-Context Learning and Model Memory

Emergent Abilities in Large Language Models (LLMs)

Practical Solutions and Value

Emergent abilities in large language models (LLMs) refer to capabilities present in larger models but absent in smaller ones. These abilities are often confused with skills gained through different prompting methods. Our research, supported by over 1000 experiments, shows that these abilities are not truly emergent but rather stem from a mix of in-context learning, memory, and language knowledge.

Pre-trained language models (PLMs) excel at learning language rules but struggle with real-world language use, which requires more complex understanding. LLMs, being larger versions of PLMs, demonstrate better performance on tasks without specific training, suggesting they have emergent abilities. However, successful task performance through techniques like in-context learning and instruction-tuning does not mean the model has an inherent ability.

The study evaluated the performance of various large language models (LLMs) across 22 tasks, revealing that while some models performed above the random baseline, the improvements were often modest and not indicative of true emergent abilities. Only five out of the 21 tasks showed significant performance differences between models, suggesting that instruction-tuning plays a crucial role in enhancing model capabilities.

This study finds that the so-called emergent abilities of large language models (LLMs) are not truly emergent but rather stem primarily from in-context learning (ICL), model memory, and linguistic knowledge. Through extensive experimentation, the authors demonstrate that LLM performance is often predictable based on smaller models or falls below the baseline, challenging the notion of robust emergent abilities.

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