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

Zhen Tan, Jie Peng, Tianlong Chen, Huan Liu

TL;DR

This work proposes an innovative metacognitive approach CLEAR, to equip LLMs with capabilities for self-aware error identification and correction, and pioneers a new path toward the trustworthiness of LLMs.

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 \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.

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

TL;DR

This work proposes an innovative metacognitive approach CLEAR, to equip LLMs with capabilities for self-aware error identification and correction, and pioneers a new path toward the trustworthiness of LLMs.

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 \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.
Paper Structure (27 sections, 10 equations, 10 figures, 6 tables)

This paper contains 27 sections, 10 equations, 10 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related work
  3. Intervention on Deep Models for Error Mitigation.
  4. Metacognitive Approaches.
  5. Methodology
  6. Concept Learning for Large Language Models
  7. Basic Setup.
  8. Incorporating Concept Bottlenecks for LLMs.
  9. Building Concept-Specific Sparse Subnetworks via Mixture of Concept Experts.
  10. Tuning-free Metacognitive Intervention
  11. Experiments
  12. Experimental Setup
  13. Datasets.
  14. Baselines.
  15. Superior Performance of CLEAR
  16. ...and 12 more sections

Figures (10)

  • Figure 1: Metacognitive LLMs are able to preceive concepts to self-correct potential errors.
  • Figure 2: The illustration of the proposed framework CLEAR, comprised of two components: (a) Concept Learinng, where the LLM backbone learns to construct concept-specific sparse networks via MoCE; and (b) Metacognitive Intervention, which involves logit entropy scrutiny, dynamic expert allocation, and pseudo intervention, and offers retrospective accountability.
  • Figure 3: Logit entropy scrutiny. It can be observed that logits of predictions with errors tend to demonstrate lower confidence and larger entropy.
  • Figure 4: Studies on using K-means for logits scrutiny. This figure illustrates the effectiveness of K-means in distinguishing between correct and erroneous logits for both routing and concept prediction. Logits are normalized via softmax, reducing the impact of noise and extreme values.
  • Figure 5: Illustration of an case study for the accountable metacognitive intervention from the IMDB-c dataset. (a) shows how CLEAR perform the intervention by allocating more experts. (b) demonstrates the rectification of the concept label prediction. (c) visualizes the contributions of different concepts.
  • ...and 5 more figures