4.6 Case study

We observe that large language models (LLMs) are more likely to perform poorly on complex tasks
such as math, code, reasoning, and complex data formats in all evaluation settings. This indicates the
limitations of these models in handling tasks that require advanced reasoning and problem-solving
skills. The challenges posed by complex reasoning problems suggest a direction for future research
and development aimed at improving the high-level capabilities of these models.
As shown in the following tables, we show the case study between WizardLM, Vicuna-7B, Alpaca
and ChatGPT. Our WizardLM demonstrates signiﬁcant improvement on such difﬁcult tasks, as it
beneﬁts from diverse and more complex instructions in its training stage. The responses of WizardLM
are more accurate, logical, and knowledgeable than the baselines.

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AlpacaTable 1: A comparison case on Code Generation skill

Skill: Code Generation Difﬁculty: 10
WizardLM- Empowering Large Language Models to Follow Complex Instructions