Metacognitive Prompting Improves Understanding in Large Language Models

• URL: http://arxiv.org/abs/2308.05342v4
• Date: Wed, 20 Mar 2024 20:37:17 GMT
• Title: Metacognitive Prompting Improves Understanding in Large Language Models
• Authors: Yuqing Wang, Yun Zhao,
• Abstract summary: We introduce Metacognitive Prompting (MP), a strategy inspired by human introspective reasoning processes. We conduct experiments on four prevalent Large Language Models (LLMs) across ten natural language understanding (NLU) datasets. MP consistently outperforms existing prompting methods in both general and domain-specific NLU tasks.
• Score: 12.112914393948415
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In Large Language Models (LLMs), there have been consistent advancements in task-specific performance, largely influenced by effective prompt design. Recent advancements in prompting have enhanced reasoning in logic-intensive tasks for LLMs, yet the nuanced understanding abilities of these models, crucial for processing and interpreting complex information, remain underexplored. In this study, we introduce Metacognitive Prompting (MP), a strategy inspired by human introspective reasoning processes. Using MP, LLMs undergo a systematic series of structured, self-aware evaluations, drawing on both their vast inherent knowledge and new insights. We conduct extensive experiments on four prevalent LLMs: Llama2, PaLM2, GPT-3.5, and GPT-4, across ten natural language understanding (NLU) datasets from GLUE, SuperGLUE, BLUE, and LexGLUE benchmarks. Additionally, we compare our method with chain-of-thought prompting and its advanced versions. The results show that GPT-4 consistently excels across all tasks, while other models have shown significant progress in some tasks when used in conjunction with MP. Furthermore, MP consistently outperforms existing prompting methods in both general and domain-specific NLU tasks. This study underscores the potential to amplify the understanding abilities of LLMs and highlights the benefits of mirroring human introspective reasoning in NLU tasks.

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