Tuning-Free Accountable Intervention for LLM Deployment - A Metacognitive Approach
Primary Area: societal considerations including fairness, safety, privacy
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Keywords: Large Language Models, Trustworthiness, Interpretability
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TL;DR: Our paper presents a metacognitive methodology for Large Language Models, offering a dynamic, concept-centric approach for efficient interventions, inspired by human perseverance in addressing intricate challenges.
Abstract: 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 *metacognitive* approach, dubbed **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 *intervention* after deployment. Our intervention offers compelling advantages:
(*i*) at deployment or inference time, our metacognitive LLMs can self-consciously identify potential mispredictions with minimum human involvement, (*ii*) the model has the capability to self-correct its errors efficiently, obviating the need for additional tuning, and (*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.
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Submission Number: 1535
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