What is the performance gap in multi-step arithmetic reasoning between quantized 4-bit LLaMA-2 models and thei

Abstract

Abstract: In this work, we introduce a novel evaluation paradigm for Large Language Models (LLMs) that compels them to transition from a traditional question-answering role, akin to a student, to a solution-scoring role, akin to a teacher. This paradigm, focusing on "reasoning about reasoning," hence termed meta-reasoning, shifts the emphasis from result-oriented assessments, which often neglect the reasoning process, to a more comprehensive evaluation that effectively distinguishes between the cognitive capabilities of different models. By applying this paradigm in the GSM8K dataset, we have developed

Research Question

What is the performance gap in multi-step arithmetic reasoning between quantized 4-bit LLaMA-2 models and their full-precision counterparts on the GSM8K benchmark?

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