ComplexityNet: Increasing LLM Inference Efficiency by Learning Task Complexity

arXiv:2312.11511v2 Announce Type: replace-cross
Abstract: We present ComplexityNet, a streamlined language model designed for assessing task complexity. This model predicts the likelihood of accurate output by various language models, each with different capabilities. Our initial application of ComplexityNet involves the Mostly Basic Python Problems (MBPP) dataset. We pioneered the creation of the first set of labels to define task complexity. ComplexityNet achieved a notable 79% accuracy in determining task complexity, a significant improvement over the 34% accuracy of the original, …

abstract application arxiv basic capabilities complexity cs.ai cs.cl cs.lg dataset efficiency inference language language model language models likelihood llm python type