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 \textit{metacognitive} approach, dubbed \textbf{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 \textit{intervention} after deployment. Our intervention offers
compelling advantages: (\textit{i})~at deployment or inference time, our
metacognitive LLMs can self-consciously identify potential mispredictions with
minimum human involvement, (\textit{ii})~the model has the capability to
self-correct its errors efficiently, obviating the need for additional tuning,
and (\textit{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.

借鉴人类认知，我们提出一种创新的元认知方法，名为 CLEAR，为大型语言模型提供具备自我感知错误鉴定和修正能力的功能。通过构建特定概念的稀疏子网络，我们的框架为模型在部署后的干预提供了一种新的接口，使其能够自觉地识别潜在的错误并高效地进行自我修正，从而增强了模型的可解释性和可访问性。通过整合这些元认知特性，我们的方法为大型语言模型的部署带来了更高的可信度和问责性。

LLM 部署的零调校可追溯干预 —— 元认知方法论

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

Despite outstanding semantic scene segmentation in closed-worlds, deep neural
networks segment novel instances poorly, which is required for autonomous
agents acting in an open world. To improve out-of-distribution (OOD) detection
for segmentation, we introduce a metacognitive approach in the form of a
lightweight module that leverages entropy measures, segmentation predictions,
and spatial context to characterize the segmentation model's uncertainty and
detect pixel-wise OOD data in real-time. Additionally, our approach
incorporates a novel method of generating synthetic OOD data in context with
in-distribution data, which we use to fine-tune existing segmentation models
with maximum entropy training. This further improves the metacognitive module's
performance without requiring access to OOD data while enabling compatibility
with established pre-trained models. Our resulting approach can reliably detect
OOD instances in a scene, as shown by state-of-the-art performance on OOD
detection for semantic segmentation benchmarks.

通过引入一种轻量级模块，利用熵量测、分割预测和空间上下文来表征分割模型的不确定性，从而实时检测像素级的极端数据，同时结合生成的合成极端数据进行最大熵训练，我们的方法可以可靠地检测场景中的极端实例，并在语义分割基准测试中表现卓越。