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Make ChatGPT even smarter using the SmartGPT framework

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Make ChatGPT even smarter using the SmartGPT framework

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Make ChatGPT even smarter using the SmartGPT framework

New frameworks are helping to make Language Learning Models (LLMs) such as ChatGPT even smarter, with the capability to tackle complex tasks autonomously three number of different stages. By dissecting task into smaller, more manageable problems, and harnessing information from the internet and other external sources. This framework known as SmartGPT is revolutionizing the way LLMs operate.

SmartLLMChains are based on the SmartGPT workflow and are a form of self-critique chain that can help you if have particularly complex questions to answer. Instead of doing a single LLM pass, it instead performs these 3 steps:

  1. Ideation: Pass the user prompt n times through the LLM to get n output proposals (called “ideas”), where n is a parameter you can set
  2. Critique: The LLM critiques all ideas to find possible flaws and picks the best one
  3. Resolve: The LLM tries to improve upon the best idea (as chosen in the critique step) and outputs it. This is then the final output.

This quick overview delves into the practical application of the SmartGPT framework in Langchain to power your own LLM Apps. The concept of SmartGPT is rooted in the implementation of self-critique or self-reflection in GPT base models to elevate the quality of generated answers.

SmartGPT and LangChain

The process is executed in three stages: ideation, critique or self-reflection, and resolve. During the ideation phase, the LLM is prompted multiple times with the same user prompt, generating a variety of outputs or ideas. The critique phase sees the LLM evaluating all the ideas it has generated, pinpointing potential flaws, and selecting the best possible answer. In the resolve phase, the LLM strives to enhance the best idea or answer it has generated, which ultimately becomes the final output.

This approach is an extension of the Chain of Thought prompting and can significantly improve the output from the LLM, particularly for prompts or questions that require logical reasoning. Langchain has incorporated a new chain, the smart LLM chain, which can be utilized in workflows.

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Higher processing costs

However, it’s worth noting that using the smart LLM chain will result in more passes and consequently higher costs than using normal prompting. For the smart LLM chain to function effectively, the underlying LLM needs to possess a self-reflection capability and be able to return just a single output.

The Idea of SmartGPT was presented by AI Explained, SmartGPT is a framework that includes self-reflection or critique from the LLM on its generated responses to think step by step and evaluate its answers before showing them to the user.

Introducing SmartGPT

Other articles you may find of interest on the subject of Langchain :

SmartGPT – Major Benchmark Broken – 89.0% on MMLU

“Has GPT4, using a SmartGPT system, broken a major benchmark, the MMLU, in more ways than one? 89.0% is an unofficial record, but do we urgently need a new, authoritative benchmark, especially in the light of today’s insider info of 5x compute for Gemini than for GPT 5?”

The SmartLLMChain can be integrated into code projects, with the detailed step-by-step process provided using code from Langchain in the first video above. This process involves importing necessary packages, setting up a prompt template, defining an LLM chain, and running the chain. The smart LLM chain can be configured with the LLM, the prompt, and the number of ideas to generate.

The LLM generates different ideas, critiques them, and then refines the best answer during the resolution phase. Different LLMs can be used for different steps, with higher temperature LLMs used for the ideation phase for more variation in responses, and lower temperature LLMs used for the critique and resolve phases.

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The unique features of SmartLLMChain lies in its innovative framework that incorporates self-reflection or critique from the LLM on its generated responses. This introspective approach enables the LLM to think step by step and evaluate its answers before presenting them to the user. This technique has proven to significantly enhance the performance of LLMs on benchmarks such as MMLU.

The SmartLLMChain is a technique that compels the LLMs to self-reflect on its answers before generating the final answer, potentially improving performance in certain use cases. This innovative approach is set to redefine the landscape of artificial intelligence and language learning models.

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