3

Metacognition is a foundational component of intelligence that has become increasingly recognized as a cornerstone of capable, transparent AI systems. While LLMs have made significant progress, it remains unclear when, how, or to what extent they can exhibit or be endowed with effective metacognitive abilities, and how such abilities can be adapted to advance the fundamental capabilities, reliability, and intelligence of AI systems. This paper bridges this gap by presenting the first systematic, comprehensive review of the current state of knowledge on metacognition for LLMs. See the GitHub repo for the full paper list!

We introduce a novel framework to systematically quantify faithful calibration—the alignment between expressed and intrinsic confidence—of large reasoning models (LRMs) in long-form reasoning settings. We analyze linguistic decisiveness relative to three sources of internal uncertainty, based on token probabilities, hidden states, and sampled response consistency. We also devise a prefix-conditioned sampling approach to control for conditional and structural variation across traces.

The first systematic study of whether epistemic markers emitted by LLMs consistently and stably reflect their intrinsic confidence. We operationalize marker internal confidence (MIC) as the internal confidence level an LLM associates with a specific epistemic marker, and introduce a suite of 7 metrics to evaluate the in- and out-of-distribution reliability of MICs across diverse models and tasks.

For LLMs to be deployed reliably and responsibly, it is essential that their linguistically expressed confidence faithfully reflect their internal uncertainty. This paper presents the first study to systematically and comprehensively benchmark faithful calibration of LLMs and proposes MetaFaith, the first method to improve faithful calibration of any instruction-following LLM in a task-agnostic manner.

This paper discusses the social implications of reinforcement learning with human feedback (RLHF), identifying key social and ethical issues and discussing social impacts for stakeholders. Seven impact areas are examined, including misinformation, AI value-alignment, bias, AI access, cross-cultural dialogue, industry, and workforce.

Weight friction draws from principles in neuroscience and physics to help mitigate catatstrophic forgetting in neural networks. It operates over multiple task domains and has convergence comparable to SGD.

GFCCs can be powerful speech features for use in speech emotion recognition. This preliminary study suggests they are more suitable versus MFCCs for emotion and intensity classification tasks with neural networks.