Pretraining a linearly parameterized single-layer linear attention model for linear regression with a Gaussian prior requires only a small number of independent tasks.
In-Context Learning (ICL) kann tatsächlich Beziehungen zwischen Etiketten aus den Beispielen in Kontexten lernen, ist aber nicht mit konventionellem Lernen gleichzusetzen.
High-capacity transformers can mimic Bayesian inference when performing in-context learning across a diverse range of linear and nonlinear function classes. The inductive bias of in-context learning is determined by the pretraining data distribution.
In-context learning (ICL) has emerged as a powerful paradigm for natural language processing, enabling large language models to make predictions based on a few demonstration examples. This survey aims to comprehensively review the progress and challenges of ICL.
대규모 언어 모델(LLM)은 인간이 작성한 것보다 더 효과적인 맥락 내 추론 예시를 생성하여 더 효과적으로 지식 추론 능력을 향상시킬 수 있다.
Large language models (LLMs) exhibit an inherent ability for density estimation, effectively approximating probability density functions from in-context data through a mechanism resembling adaptive kernel density estimation.
대규모 언어 모델(LLM)은 In-Context 학습을 통해 확률 밀도 함수(PDF)를 추정하는 능력을 보이며, 이는 적응형 커널 밀도 추정(KDE)으로 해석될 수 있습니다.
In-context learning (ICL) effectiveness in large language models (LLMs) depends on the interplay between the model's ability to recognize the task and the presence of similar examples in demonstrations, forming four distinct scenarios within a two-dimensional coordinate system.
The accuracy of in-context learning (ICL) in large language models (LLMs) for binary classification tasks is a complex interplay between pre-training knowledge, the number and quality of in-context examples, and potential dependencies among examples.
Neural networks capable of both in-context learning (ICL) and traditional in-weight learning (IWL) can exhibit dual learning behaviors observed in humans: demonstrating compositional generalization and a blocking advantage in rule-governed tasks, while exhibiting an interleaving advantage in tasks lacking such structure.