Tuning-Free Accountable Intervention for LLM Deployment -- A Metacognitive Approach

Large Language Models (LLMs) have catalyzed transformative advances across a spectrum of natural language processing tasks through few-shot or zero-shot prompting, bypassing the need for parameter tuning. While convenient, this modus operandi aggravates hallucination'' concerns, particularly given the enigmatic black-box’’ nature behind their gigantic model sizes. Such concerns are exacerbated in high-stakes applications (e.g., healthcare), where unaccountable decision errors can lead to devastating consequences. In contrast, human decision-making relies on nuanced cognitive processes, such as the ability to sense and adaptively correct misjudgments through conceptual understanding. Drawing inspiration from human cognition, we propose an innovative $\mathit{\text{metacognitive}}$ approach, dubbed $\mathbf{\text{CLEAR}}$, to equip LLMs with capabilities for self-aware error identification and correction. Our framework facilitates the construction of concept-specific sparse subnetworks that illuminate transparent decision pathways. This provides a novel interface for model $\mathit{\text{intervention}}$ after deployment. Our intervention offers compelling advantages: ($\mathit{\text{i}}$)~at deployment or inference time, our metacognitive LLMs can self-consciously identify potential mispredictions with minimum human involvement, ($\mathit{\text{ii}}$)~the model has the capability to self-correct its errors efficiently, obviating the need for additional tuning, and ($\mathit{\text{iii}}$)~the rectification procedure is not only self-explanatory but also user-friendly, enhancing the interpretability and accessibility of the model. By integrating these metacognitive features, our approach pioneers a new path toward engendering greater trustworthiness and accountability in the deployment of LLMs.