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In-Context Learning (ICL): Rather than fine tuning a model, LLMs can take a small sample of your data and, within the context of the data you provide it temporarily learns the data. The difference here between fine tuning and ICL is that you don't update the weights with ICL. Article - In-Context Learning Approaches in Large Language Models
Summary: In-Context Learning (ICL) and Its Approaches in Large Language Models
Introduction to In-Context Learning (ICL):
ICL allows Large Language Models (LLMs) to learn and adapt from a few examples given within a context. Unlike traditional supervised learning, ICL doesn't necessitate model parameter updates.
The foundational principle of ICL is "learning from analogy". It involves concatenating a query with a demonstration context to prompt the language model for prediction.
Advantages of ICL include:
An interpretable interface via natural language examples.
Reduced computational costs due to its training-free framework.
The capacity to seamlessly integrate human knowledge into LLMs. Reference 2
Key Approaches to ICL:
Chain of Thought (CoT):
Designed to enhance standard input-output prompting, particularly for intricate reasoning tasks.
Incorporates intermediate reasoning steps guiding to the final outcome.
Best suited for larger models and excels in tasks necessitating sequential reasoning.
Its efficacy might be tied to models trained on coding datasets, fostering enhanced reasoning abilities. Reference 6
Zero-shot CoT:
A variant of CoT devoid of example reliance. Uses guiding prompts such as "Let’s think step by step" to steer the model's reasoning.
Comprises two stages: "reasoning prompt extraction" for deriving a reasoning path and "answer prompt extraction" to procure the final response. Reference 7
Self-consistency CoT:
Introduces a "sample-and-marginalize" method as an alternative to the standard greedy decoding in CoT.
Generates multiple reasoning paths and elects the most consistent answer from them, similar to majority voting.
Analogous to human cognition where confidence in an answer escalates when multiple reasoning paths converge to the same conclusion. Reference 9
Tree of Thoughts (ToT):
A generalization of CoT, allowing LMs to traverse multiple reasoning paths.
Comprises four main components: thought decomposition, thought generation, state evaluation, and a search algorithm.
ToT's adaptability and modularity make it a powerful tool for problem-solving with LMs. Reference 10
Automatic Prompting Techniques:
Prompt engineering, which involves crafting prompts to guide LLMs, is an empirical science that varies significantly across models and requires intensive experimentation.
Given the complexities and manual efforts involved in prompt engineering, there's an active push towards automating this process. Two notable attempts include:
Automatic Prompt Augmentation and Selection COT: Proposes a three-step method – augment, prune, and select – to automate CoT prompting. Reference 11
Auto-CoT: Aims to automatically construct demonstrations with questions and reasoning chains. It employs clustering techniques for question sampling and then generates chains. Reference 12
Conclusion:
In-context learning facilitates intuitive communication with LLMs, steering their behavior for desired results. Given the complexities of manual prompt engineering, the push towards automation in this realm is promising. However, it still necessitates significant experimentation and adaptations for real-world applications.
For a deeper understanding of the various topics discussed:
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In-Context Learning (ICL): Rather than fine tuning a model, LLMs can take a small sample of your data and, within the context of the data you provide it temporarily learns the data. The difference here between fine tuning and ICL is that you don't update the weights with ICL. Article - In-Context Learning Approaches in Large Language Models
Summary: In-Context Learning (ICL) and Its Approaches in Large Language Models
Introduction to In-Context Learning (ICL):
Reference 2
Key Approaches to ICL:
Chain of Thought (CoT):
Reference 6
Zero-shot CoT:
Reference 7
Self-consistency CoT:
Reference 9
Tree of Thoughts (ToT):
Reference 10
Automatic Prompting Techniques:
Reference 11
Reference 12
Conclusion:
In-context learning facilitates intuitive communication with LLMs, steering their behavior for desired results. Given the complexities of manual prompt engineering, the push towards automation in this realm is promising. However, it still necessitates significant experimentation and adaptations for real-world applications.
For a deeper understanding of the various topics discussed:
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