ATOMIC CHAIN OF THOUGHT



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Overview

The "Chain of Atomic Queries" is an innovative approach to handling complex user queries by breaking them down into simpler, more manageable components. This method employs an "Expert LLM" (Large Language Model) to dissect a user's complex query into a series of "atomic queries," each representing a focused task or question. A "Benchmark Matching Engine" then selects the most appropriate AI model from a diverse range of options to process each atomic query, ensuring optimal handling of each component. This system incorporates an evaluation layer to assess the accuracy and quality of responses, enhancing the overall reliability of the final answer. Unlike the traditional chain-of-thought approach, which is primarily sequential, the Chain of Atomic Queries method allows for both sequential and parallel processing of queries, as determined by the expert model. This flexibility and efficiency in query processing enable the system to leverage the strengths of various models, resulting in more accurate and comprehensive responses to complex user inquiries.

Understanding the "Chain of Atomic Queries"

The "Chain of Atomic Queries" is a sophisticated approach to processing complex user queries by breaking them down into smaller, more manageable parts called "atomic queries". Here's a brief explanation:
    .1Query Breakdown: When a user submits a query, it's first processed by an "Expert LLM" (Large Language Model). This LLM is specifically fine-tuned to specialize in the "chain of thought" approach for query decomposition.
    .2Atomic Queries: The Expert LLM breaks down the complex user query into a series of simpler, "atomic" queries. Each atomic query represents a single, focused task or question that's part of the larger user query.
    .3Model Selection: For each atomic query, the system selects the most appropriate AI model to handle it. This selection is done by a "Benchmark Matching Engine" which determines the optimal model based on the query's specific requirements and the models' capabilities.
    .4Diverse Models: The system uses a variety of models (like Mistral-7B, Llama-3.1-8b, Llama-3.1-70b, etc.) to process different atomic queries, ensuring each part of the complex query is handled by the most suitable model.
    .5Evaluation Layer: An additional "Evaluation layer" is implemented to assess the accuracy and quality of responses for each atomic query. This layer helps ensure the overall quality of the final answer.
    .6Flexibility and Efficiency: This approach allows for more flexible and efficient processing of complex queries, as it can leverage the strengths of different models for different aspects of the query, rather than relying on a single model for everything.

How does it differ from Chain-of-thought approach?

  • Chain-of-thought is mostly sequential, which is not efficient, as the queries in the chain need to wait until the last query is completed, then execute.
  • Atomic queries are partial sequential and partial parallel, which is more efficient, the expert model will decide which part of atomic queries need to execute sequentially and which part will be executed in parallel,