Difference between revisions of "In-Context Learning (ICL)"
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* [https://thegradient.pub/in-context-learning-in-context/ In Context | Daniel Bashir - The Gradient] | * [https://thegradient.pub/in-context-learning-in-context/ In Context | Daniel Bashir - The Gradient] | ||
* [https://www.marktechpost.com/2023/07/14/think-like-this-and-answer-me-this-ai-approach-uses-active-prompting-to-guide-large-language-models/ Think Like this and Answer Me: This AI Approach Uses Active Prompting to Guide Large Language Models | Ekrem Çetinkaya - MarketTechPost] | * [https://www.marktechpost.com/2023/07/14/think-like-this-and-answer-me-this-ai-approach-uses-active-prompting-to-guide-large-language-models/ Think Like this and Answer Me: This AI Approach Uses Active Prompting to Guide Large Language Models | Ekrem Çetinkaya - MarketTechPost] | ||
| + | * [https://lastweekin.ai/p/how-in-context-learning-emerges How In-Context Learning Emerges | Jacky Liang - Last Week in AI] ... [[Large Language Model (LLM)|LLM]]s have been able to do something more powerful: learn new tasks and skills simply from new examples in the input, without any gradient updates or changes to the pretrained model. | ||
In-context[[context]] learning (ICL) is a paradigm in [[Natural Language Processing (NLP)]] where [[Large Language Model (LLM)]] make predictions based on [[context]]s augmented with just a few training examples. [[Large Language Model (LLM)|LLM]]s are able to extract patterns from the examples provided in the [[context]] and use them to perform many complex [[Natural Language Processing (NLP)|NLP]] tasks. - [https://theglobalnlplab.substack.com/p/in-context-learning-a-new-paradigm In-context Learning - A New Paradigm in NLP? | The Global NLP Lab] | In-context[[context]] learning (ICL) is a paradigm in [[Natural Language Processing (NLP)]] where [[Large Language Model (LLM)]] make predictions based on [[context]]s augmented with just a few training examples. [[Large Language Model (LLM)|LLM]]s are able to extract patterns from the examples provided in the [[context]] and use them to perform many complex [[Natural Language Processing (NLP)|NLP]] tasks. - [https://theglobalnlplab.substack.com/p/in-context-learning-a-new-paradigm In-context Learning - A New Paradigm in NLP? | The Global NLP Lab] | ||
Revision as of 17:38, 21 July 2023
YouTube ... Quora ...Google search ...Google News ...Bing News
- In-Context Learning (ICL) ... Context ... Causation vs. Correlation ... Autocorrelation ... Out-of-Distribution (OOD) Generalization ... Transfer Learning
- Singularity ... Sentience ... AGI ... Curious Reasoning ... Emergence ... Moonshots ... Explainable AI ... Automated Learning
- Generative AI ... Conversational AI ... ChatGPT | OpenAI ... Bing | Microsoft ... Bard | Google ... Claude | Anthropic ... Perplexity ... You ... Ernie | Baidu
- Large Language Model (LLM) ... Natural Language Processing (NLP) ...Generation ... Classification ... Understanding ... Translation ... Tools & Services
- Chain of Thought (CoT) ... Tree of Thoughts (ToT)
- Learning Techniques
- Few Shot Learning
- A Survey on In-context Learning | Q. Dong, L. Li, D. Dai, C. Zheng, Z. Wu, B. Chang, X. Sun, J. Xu, L. Li, Z. Sui - arXiv Cornell University
- Context-based learning | Wikipedia
- How does GPT do in-context learning? | Kushal Shah - Medium
- In Context | Daniel Bashir - The Gradient
- Think Like this and Answer Me: This AI Approach Uses Active Prompting to Guide Large Language Models | Ekrem Çetinkaya - MarketTechPost
- How In-Context Learning Emerges | Jacky Liang - Last Week in AI ... LLMs have been able to do something more powerful: learn new tasks and skills simply from new examples in the input, without any gradient updates or changes to the pretrained model.
In-contextcontext learning (ICL) is a paradigm in Natural Language Processing (NLP) where Large Language Model (LLM) make predictions based on contexts augmented with just a few training examples. LLMs are able to extract patterns from the examples provided in the context and use them to perform many complex NLP tasks. - In-context Learning - A New Paradigm in NLP? | The Global NLP Lab
Why can LLMs learn to do something entirely new by merely being shown a few input-output examples?
Prompt: Given the criteria for determining whether something is true or false, based on whether or not it is found in the sky.
bird is true, plane is true, car is false, truck is false,
is train true or false?
Mystery: Why does ICL work so well?
There have been a few studies aiming to uncover this in the literature.
- One factor that might play a role is the distribution of the training data. When training on a very large dataset, the ICL ability of LLMs seems to emerge when the data appears in clusters and there are a sufficient number of rare classes present.
- Another factor is that Transformer models might be learning to encode learning algorithms implicitly during the training process, due to the properties of their architecture. During inference, transformer LLMs might be performing an implicit finetuning using the provided examples in the context.
In-context learning works well because it allows users to quickly build models for a new use case without worrying about fine-tuning and storing new parameters for each task. It typically requires very few training examples to get a prototype working, and the natural language interface is intuitive even for non-experts. How does in-context learning work? A framework for understanding the differences from traditional supervised learning | Sang Michael Xie and Sewon Min - The Stanford AI Lab
Pretraining vs Fine-tuning vs In-context Learning (ICL) of LLM
YouTube ... Quora ...Google search ...Google News ...Bing News
| Method | Description |
|---|---|
| Pretraining | ... training a model on a large dataset to learn general language patterns. This is usually done using an unsupervised or self-supervised objective such as predicting the next word in a sequence. |
| Fine-tuning | ... taking a pretrained model and further training it on a smaller dataset specific to a particular task. This allows the model to adapt to the specific task while still leveraging the general language patterns learned during pretraining. |
| In-context learning (ICL) | ... involves using a pretrained model to make predictions based on contexts augmented with just a few training examples. The model is able to extract patterns from the examples provided in the context and use them to perform many complex NLP tasks without any additional training. |
In-context Reinforcement Learning
YouTube ... Quora ...Google search ...Google News ...Bing News
- Reinforcement Learning (RL)
- Assistants ... Personal Companions ... Agents ... Negotiation ... LangChain
- In-context Reinforcement Learning with Algorithm Distillation | M. Laskin, L. Wang, J. Oh, E. Parisotto, S. Spencer, R. Steigerwald, DJ Strouse, S. Hansen, A. Filos, E. Brooks, M. Gazeau, H. Sahni, S. Singh, V. Mnih - arXiv - Cornell University
