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A Survey of Techniques for Maximizing LLM Performance (OpenAI Dev Day 2023): Difference between revisions
A Survey of Techniques for Maximizing LLM Performance (OpenAI Dev Day 2023) (view source)
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|Website = https://www.youtube.com/watch?v=ahnGLM-RC1Y | |Website = https://www.youtube.com/watch?v=ahnGLM-RC1Y | ||
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==TLDR== | |||
[[Optimizing LLM performance]] is a complex, iterative process that involves a combination of [[prompt engineering]], [[RAG]], and [[fine-tuning]]. Each technique addresses specific optimization needs and challenges, and their effective combination can significantly enhance LLM capabilities. The journey from initial prompt engineering to fine-tuning represents a comprehensive approach to LLM optimization, underscored by practical insights and real-world applications. | |||
==Introduction== | ==Introduction== | ||
This article explores the techniques for maximizing the performance of Large Language Models (LLMs) like those developed by OpenAI. The insights are drawn from the experiences of John Allard, an engineering lead at OpenAI, and Colin, head of solutions practice in Europe, shared during OpenAI's first developer conference. | This article explores the techniques for maximizing the performance of [[Large Language Models]] (LLMs) like those developed by [[OpenAI]]. The insights are drawn from the experiences of [[John Allard]], an engineering lead at OpenAI, and [[Colin]], head of solutions practice in Europe, shared during OpenAI's first developer conference. | ||
==Background== | ==Background== | ||
LLMs have revolutionized the field of natural language processing, offering unprecedented capabilities in understanding and generating human-like text. However, optimizing these models for specific tasks remains a challenge. The focus is on understanding and applying various techniques to enhance LLM performance. | LLMs have revolutionized the field of [[natural language processing]], offering unprecedented capabilities in understanding and generating [[human-like text]]. However, optimizing these models for specific tasks remains a challenge. The focus is on understanding and applying various techniques to enhance LLM performance. | ||
==Prompt Engineering== | ==Prompt Engineering== | ||
Prompt engineering involves crafting inputs to guide the LLM's response in a desired direction. It is an effective starting point for LLM optimization, allowing for rapid testing and learning. Key strategies include: | [[Prompt engineering]] involves crafting inputs to guide the LLM's response in a desired direction. It is an effective starting point for LLM optimization, allowing for rapid testing and learning. Key strategies include: | ||
#Writing clear instructions | #Writing clear instructions | ||
#Breaking complex tasks into simpler subtasks | #Breaking complex tasks into simpler subtasks | ||
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==Retrieval-Augmented Generation (RAG)== | ==Retrieval-Augmented Generation (RAG)== | ||
RAG extends the capabilities of LLMs by combining their predictive power with external knowledge sources. It involves retrieving relevant information from a database or knowledge base and presenting it to the LLM along with the query. This approach helps in: | [[RAG]] extends the capabilities of LLMs by combining their predictive power with external knowledge sources. It involves retrieving relevant information from a [[database]] or [[knowledge base]] and presenting it to the LLM along with the query. This approach helps in: | ||
#Introducing new information | #Introducing new information | ||
#Reducing hallucinations by controlling content | #Reducing hallucinations by controlling content | ||
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==Fine-Tuning== | ==Fine-Tuning== | ||
Fine-tuning is a transformative process where an existing LLM is further trained on a specific, often smaller and more domain-specific dataset. It offers two primary benefits: | [[Fine-tuning]] is a transformative process where an existing LLM is further trained on a specific, often smaller and more domain-specific [[dataset]]. It offers two primary benefits: | ||
#Achieving higher performance levels | #Achieving higher performance levels | ||
#Enhancing efficiency during model interaction | #Enhancing efficiency during model interaction | ||
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==Practical Applications and Case Studies== | ==Practical Applications and Case Studies== | ||
The techniques were applied to the Spider 1.0 benchmark, which involves generating SQL queries from natural language descriptions. The journey involved starting with prompt engineering, moving to RAG, and eventually fine-tuning with the help of partners at Scale AI. The process exemplified the non-linear nature of LLM optimization and the need for multiple iterations to achieve the desired performance | The techniques were applied to the [[Spider 1.0]] benchmark, which involves generating [[SQL queries]] from [[natural language descriptions]]. The journey involved starting with prompt engineering, moving to RAG, and eventually fine-tuning with the help of partners at [[Scale AI]]. The process exemplified the non-linear nature of LLM optimization and the need for multiple iterations to achieve the desired performance. | ||