EVOR Knowledge Base Evolution and LLM Reasoning Performance in MultiPL-E

Abstract

Abstract: Large Language Models (LLM) with reasoning capabilities offer a promising path for improving candidate evaluation in planning frameworks, but their relative performance against traditional non-reasoning models remains largely underexplored. In this study, we benchmark a distilled 1.5B parameter reasoning model (DeepSeek-R1) against several state-of-the-art non-reasoning LLMs within a generator-discriminator LLM planning framework for the text-to-SQL task. For this, we introduce a novel method for extracting soft scores from the chain-of-thought (CoT) outputs from reasoning that enables fine-gr

Research Question

What is the impact of EVOR's diverse knowledge base evolution on the reasoning capability of LLMs as measured by functional correctness scores on the MultiPL-E Python and Java subsets?

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Provenance