【In-Context Learning】Meta-learning via Language Model In-context Tuning - 代码天地

【In-Context Learning】Meta-learning via Language Model In-context Tuning

业界资讯 2023-04-06 17:30:29 阅读次数: 0

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转载自blog.csdn.net/qq_36426650/article/details/128083896

【In-Context Learning】Meta-learning via Language Model In-context Tuning

Meta-Learning and in-context Learning

In-Context Learning玩法大全

RAVEN: In-Context Learning with Retrieval Augmented Encoder-Decoder Language Models

In-Context Learning中的示例选择及效果

Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

【In-Context Learning】Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

【In-Context Learning】What Makes Good In-Context Examples for GPT-3?

论文略读：Can We Edit Factual Knowledge by In-Context Learning?

总结在Prompt Engineering中的ICL(In-Context Learning)方法

什么是 prompts, completions, tokens and meta-learning /in-context-learning

【Chain-of-Thought 专题】The Unreliability of Explanations in Few-Shot In-Context Learning

论文浅尝 | 大语言模型在in-context learning中的不同表现

In-Context Learning开卷视觉任务，DeepMind提出快速适应新任务的“蜂鸟”模型

EMNLP 2023 | DeepMind提出大模型In-Context Learning的可解释理论框架

In-context learning如何工作？斯坦福学者用贝叶斯方法解开其奥秘

Rethinking the Role of Demonstrations: What Makes In-Context LearningWork?

One-Shot Visual Imitation Learning via Meta-Learning

MASSIVE EDITING FOR LARGE LANGUAGE MODELS VIA META LEARNING

Learning to Adapt: Meta-Learning for Model-Based Control

大语言模型的预训练[5]：语境学习、上下文学习In-Context Learning：精调LLM、Prompt设计和打分函数（Scoring Function）设计以及ICL底层机制等原理详解

Model-Agnostic Meta-Learning (MAML) 理解

OUTFOX: LLM-Generated Essay Detection Through In-Context Learningwith Adversarially Generated Examp

An End-to-End Approach to Natural Language Object Retrieval via Context-Aware Deep Reinforcement Lea

深度学习课程笔记（十七）Meta-learning (Model Agnostic Meta Learning)

论文分享|《Universal Language Model Fine-tuning for Text Classificatio》

《Universal Language Model Fine-tuning for Text Classification》翻译

Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning

Few-Shot Domain Adaptation for Grammatical Error Correction via Meta-Learning翻译

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