AutoMix is an revolutionary approach that optimises the allocation of queries to larger language models (LLMs) by assessing the approximate correctness of responses from a smaller LM. It incorporates a few-shot self-verification process and a meta-verifier to boost accuracy. AutoMix showcases its efficiency in balancing computational cost and performance in language processing tasks.

In terms of verifying information, AutoMix takes a distinct approach than other methods. Relatively than solely counting on LLM knowledge, it uses context to make sure accuracy. Its unique few-shot self-verification mechanism and meta-verifier assess the reliability of its output without requiring any training. This emphasis on context and robust self-verification aligns with conformal prediction. Unlike other approaches that require verifier training or architectural modifications, AutoMix provides flexibility between models and only requires black-box access to APIs.

The iterative model-switching method utilized by the problem-solving approach AutoMix involves querying models of various sizes and capabilities, with feedback verification at each step to find out whether to simply accept the output or switch to a more capable model. This approach doesn’t need separate models or access to model weights and gradients, because it utilises black-box language model APIs. The method is more efficient and effective by introducing few-shot learning and self-verification for solution generation, verification, and model switching.

AutoMix employs a few-shot self-verification process to evaluate its output reliability without training. It enhances accuracy with a meta-verifier. Queries are categorised into Easy, Complex, or Unsolvable using a Partially Observable Markov Decision Process (POMDP) framework. AutoMix intelligently routes queries to larger language models based on approximate output correctness from smaller models. The Incremental Profit Per Unit Cost (IBC) metric quantifies the efficiency of mixing smaller and bigger language models, optimising computational cost and performance in language processing tasks.

Through context-grounded reasoning, AutoMix has significantly enhanced IBC (Intentional Behaviour Change) performance, outperforming baseline methods by as much as 89% across five datasets. The meta-verifier included on this tool consistently shows superior IBC performance, particularly within the LLAMA2-1370B datasets. The highest performer in three of 5 datasets is AutoMix-POMDP, which offers significant improvements in most of them. It maintains a positive IBC across all evaluated costs, indicating consistent enhancements. The POMDP-based meta-verifier in AutoMix has also been shown to outperform Verifier-Self-Consistency by as much as 42% across all datasets.

In conclusion, AutoMix is a promising framework that effectively combines black-box LLM APIs in a multi-step problem-solving approach. Its self-verification and context-grounded few-shot verification exhibit a great balance between performance and computational cost, making it suitable for various scenarios. Moreover, integrating a POMDP in AutoMix enhances the accuracy of the few-shot verifier, highlighting its potential to enhance the performance of LLM during inference. Overall, AutoMix shows promising capabilities for language processing tasks.

Future research can explore AutoMix’s application in various domains and tasks to evaluate its versatility. Evaluating AutoMix’s performance with diverse language model combos is crucial, ensuring scalability to larger models. Refinement of the few-shot self-verification mechanism, potentially incorporating contextual or external information, is required for improved accuracy. Alternative meta-verifiers or verification techniques will be investigated to boost AutoMix. User studies are essential to judge AutoMix’s practical usability and user satisfaction in real-world scenarios.

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