AutoMix is an innovative approach to allocating queries to language models (LLMs) based on the correctness of responses. It uses context and self-verification to ensure accuracy, and can switch between different models. AutoMix enhances performance and computational cost in language processing tasks and demonstrates promising capabilities for future research and application.
**Optimizing Computational Costs with AutoMix: An AI Strategic Approach to Leveraging Large Language Models from the Cloud**
AutoMix is an innovative approach that improves the allocation of queries to language models by ensuring accuracy and balancing computational cost. It uses a few-shot self-verification process and a meta-verifier to enhance performance.
Unlike other methods, AutoMix doesn’t solely rely on large models’ knowledge. It emphasizes context to ensure accuracy. It assesses the reliability of its output using a unique few-shot self-verification mechanism and meta-verifier, without requiring any training. This approach aligns with conformal prediction and offers flexibility between models.
AutoMix employs an iterative model-switching method, querying models of different sizes and capabilities. It uses feedback verification to determine whether to accept the output or switch to a more capable model. This approach doesn’t require separate models or access to model weights and gradients, making it efficient and effective.
By categorizing queries into Simple, Complex, or Unsolvable, AutoMix intelligently routes queries to larger language models based on approximate output correctness from smaller models. It optimizes computational cost and performance using the Incremental Benefit Per Unit Cost (IBC) metric.
Through context-grounded reasoning, AutoMix significantly enhances IBC performance, outperforming baseline methods by up to 89% across five datasets. The meta-verifier included in AutoMix consistently shows superior performance, especially in the LLAMA2-1370B datasets. AutoMix-POMDP, the top performer in three of five datasets, offers significant improvements. The POMDP-based meta-verifier also outperforms Verifier-Self-Consistency by up to 42% across all datasets.
In conclusion, AutoMix effectively combines black-box language models in a problem-solving approach, achieving a good balance between performance and computational cost. Its self-verification and few-shot verification mechanisms make it suitable for various scenarios. Integration of a POMDP enhances accuracy, showing potential for improving language models. Future research can explore its application in different domains and tasks, evaluate its performance with diverse language model combinations, refine the verification mechanism, and conduct user studies.
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