Conceitos Básicos
In-context learning exhibits dual operating modes: task learning and task retrieval, explained by a probabilistic model.
Resumo
The content explores the dual operating modes of in-context learning, introducing a probabilistic model to explain task learning and task retrieval. It delves into the behavior of the optimally pretrained model, shedding light on real-world phenomena observed with large language models. The analysis includes the impact of in-context examples on the posterior distribution, the explanation of the "early ascent" phenomenon, and the bounded efficacy of biased-label in-context learning. Experimental validation with Transformers and language models is conducted.
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Introduction
- Large language models show improvement with in-context learning.
- Dual operating modes: task learning and task retrieval.
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New Model for Pretraining Data
- Proposes a probabilistic model for pretraining data and in-context examples.
- Extends existing models for linear functions with multiple task groups.
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Analysis
- Analyzes the optimal pretrained model under the squared loss.
- Derives the closed-form expression of the task posterior distribution.
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Explanation of Two Real-World Phenomena
- Discusses the "early ascent" phenomenon observed in practice.
- The bounded efficacy of biased-label in-context learning is theoretically justified.
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Inquiry and Critical Thinking
- How does the probabilistic model enhance the understanding of dual operating modes?
- What implications do the findings have for the practical application of in-context learning?
- How can the insights from this study be applied to improve the performance of large language models?
Estatísticas
최적 사전 훈련 모델의 행동을 분석하여 ICL 위험 상한을 유도합니다.
ICL 위험 상한은 특정 조건에서 증가하고 감소합니다.
Citações
"ICL exhibits dual operating modes: task learning and task retrieval."
"Recent theoretical work investigates various mathematical models to analyze ICL."