Metacognitive Prompting Improves Understanding in Large Language Models (2024)

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.

## introduction large language models (llms) have made significant advancements in natural language processing (nlp) in recent years @xcite @xcite . however, as these models progress, simply increasing their scale does not necessarily enhance their understanding and reasoning capabilities @xcite . delving into the intricacies of prompt design has emerged as a promising approach; it not only rivals the benefits of extensive fine-tuning but also offers clear advantages in sample efficiency @xcite . many research efforts have extensively explored prompt design, particularly emphasizing the use of chain-of-thought (cot) @xcite approaches to advance intermediate reasoning steps. this led to variants such as least-to-most @xcite , self-consistency @xcite , and tree-of-thoughts (tot) @xcite techniques. these strategies are effective in designated contexts where the main objective centers around enhancing explicit reasoning capacities in areas like arithmetic, commonsense, and symbolic reasoning, guiding llms through a logical progression of thought. however, their effectiveness in deepening understanding is limited, as reasoning involves methodically connecting concepts, whereas understanding requires grasping underlying semantics and broader contextual meanings. to bridge the gap in enhancing llms' understanding abilities, crucial for solving complex tasks, we propose metacognitive prompting (mp). this method is informed by the concept of metacognition, often defined as 'thinking about thinking'. derived from cognitive psychology, metacognition relates to an individual's awareness and selfreflection on their cognitive processes. our approach integrates key aspects of human metacognitive processes into llms. figure 1 shows the parallels between human metacognitive stages and the operational steps of our method in llms. rather than concentrating solely on the mechanics of "how" a response is produced, this method delves deeper into the rationale or "why" behind it. the method proceeds as follows: 1) the llm interprets the provided text, a phase reminiscent of human comprehension; 2) the model then forms an initial judgment, mirroring the stage in which humans generate judgments based on information; 3) the llm subjects its preliminary inference to critical evaluation, a step aligned with the self-reflection that humans engage in during cognitive processes; 4) after this introspective assessment, the model finalizes its decision and elucidates its reasoning, similar to human decision-making and rationalization; 5) finally, the llm gauges its confidence in the outcomes, reflecting how humans evaluate the credibility of their judgments and explanations. this paradigm elevates the model's function beyond simple systematic reasoning, compelling it to participate in introspective evaluations that determine the depth and relevance of its responses. we conducted experiments on ten nlu datasets from glue @xcite , super-glue @xcite , blue @xcite , and lexglue @xcite benchmarks using several leading llms, including llama2 @xcite , palm2 @xcite , @xcite . our empirical evaluations underscore the superiority of mp over existing prompting strategies, including cot and its variants. this work emphasizes the importance of incorporating human-inspired introspective reasoning into llms, shedding light on an approach that deepens their understanding abilities. in summary, our contributions are threefold: ## related work our proposal for metacognitive prompting is informed by several foundational trajectories: the evolving paradigms of prompting within llms, advancements in nlu in the broader nlp domain, and the intricate interplay between cognitive processes and nlu dynamics. ## metacognitive prompting in the complex terrain of human cognition, our ability to introspect and regulate our thinking processes stands as a keystone for intricate problem-solving and decision-making. this high-level cognition underlies our proficiency in breaking down abstract concepts, critically evaluating scenarios, and finetuning our reasoning. the primary aim of this work is to equip llms with a process that simulates the self-reflective cognitive process. in doing so, we aim to improve llms' capabilities in interpreting and responding to nlu tasks. we propose mp, which instills critical elements of human metacognition into llms. this approach involves five distinct stages: 1) the llm begins by deciphering the input text to comprehend its context and meaning, mirroring the initial comprehension stage in human thought; 2) it then forms a preliminary interpretation of the text, a step that reflects judgment formation in humans; 3) subsequently, the llm critically evaluates this initial judgment for accuracy, akin to the self-scrutiny humans apply during problem-solving; 4) after this evaluation, the llm finalizes its decision and offers an explanation for its reasoning, aligning with the decision-making and rationalization phase in human cognition; 5) ultimately, the llm assesses its confidence in the outcome of the entire process, similar to how humans gauge the certainty of their decisions and explanations. figure 2 provides a schematic representation of our mp. it outlines the five sequential metacognitive stages, the specific prompts directed at the llm, and corresponding model outputs. in essence, mp introduces a structured approach that enables llms to process tasks, enhancing their contextual awareness and introspection in responses. by systematically guiding models through stages that emulate human cognitive processes, this method offers a fresh perspective on addressing complex natural language tasks. it reshapes our perception and utilization of llms' capabilities, ushering in a paradigm where models not only grasp the intricacies of given tasks but also critically evaluate and adjust their responses. this approach establishes a foundation for more effective and reliable interactions between users and llms, particularly benefiting those with limited llm expertise, as it simplifies complex linguistic and cognitive processes into more manageable forms. sample mp templates and exemplars are shown in appendix a. ## experiments we conduct experiments on ten diverse nlu datasets selected from glue @xcite , superglue @xcite , blue @xcite , and lexglue @xcite benchmarks. we evaluate the impact of mp in comparison with cot and its variants, across four leading llms. we report the best result after mul-tiple experimental iterations. ## results in our empirical evaluations, we compare performance across all datasets and models, considering the various prompting methods used. we also investigate the efficacy of different prompting strategies, analyze errors associated with mp, and examine the relationship between confidence scores and predictive performance when mp is applied. ## limitations while our proposed mp demonstrates potential by integrating introspective features reminiscent of human cognition into llms to enhance their understanding capacities, our study does have its limitations. first, designing the prompts requires manual effort to guide the llms through metacognitive processes. second, we evaluate the effectiveness of mp using a selection of datasets and models, which may limit the broader applicability of our findings. furthermore, although the verbalized confidence of llms offers a window into their perceived certainty levels, it might not serve as the definitive method for comprehensively gauging their true confidence. a hybrid approach, such as combining verbalization with self-consistency checks, could offer a more robust method for confidence calibration. additionally, our study does not extensively address vital ethical and legal concerns, such as potential biases, privacy implications, and fairness challenges. future research on mp will address these dimensions to ensure the responsible and holistic application of llms in different areas. ## discussion in this study, we present mp to infuse introspective features that mirror human cognition into llms. the mp process involves five distinct stages: it starts by comprehending the input text, then moves to formulate an initial judgment. next, it critically reevaluates this initial impression, settles on a decision while explaining its rationale, and finally gauges its confidence in the decisions made. we conduct experiments on a broad range of datasets from several popular nlu benchmarks and evaluate several prominent llms with different prompting methods. the results underscore the potential of our method, demonstrating advantages over existing prompting methods. through our analysis, specific error patterns associated with mp are identified, highlighting nuances in comprehension and judgment stages that warrant further refinement. while mp provides a structured pathway for models to introspect, it follows predefined stages, lacking adaptability based on real-time feedback. the five-stage design of mp, although foundational, suggests room for more intricate frameworks that might emulate human-like cognitive feedback loops more authentically. looking forward, several areas warrant further exploration. first, we plan to apply mp more broadly, particularly to detail-oriented areas such as mental health support, as well as to complex reasoning tasks like arithmetic and commonsense reasoning. refining mp could elicit more detailed introspective responses from llms. moreover, reliance on verbalized confidence can be augmented by integrating other methods for a more comprehensive assessment. additionally, the broader implications of introducing introspective llms, particularly regarding biases and the reliability of out-puts, require in-depth examination. in essence, our initial venture with mp lays a solid foundation, but significant opportunities remain to draw closer parallels between introspection in llms and natural human introspection, which can lead to more explainable and accountable ai systems. ## ethnics statement there are no ethics-related issues in this paper. the data and resources utilized in this work are opensource and widely used in many existing studies.