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Tuning-Free Accountable Intervention for LLM Deployment – a Metacognitive Approach
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Proposes CLEAR, a tuning-free metacognitive intervention for LLMs that builds concept-specific mixture-of-concept-experts subnetworks, detects likely errors via logit entropy, and allocates extra experts at inference.
Problem
Post-deployment intervention on LLM mispredictions in high-stakes text classification/regression, where existing methods need human error labels, parameter fine-tuning, or incur high inference latency.
Approach
Concept-bottleneck LLMs with a Mixture of Concept Experts (MoCE) layer give interpretable, concept-specific sparse pathways; logit entropy with K-Means thresholds flags likely errors automatically, and the router's top-T is raised to T' at inference, with a pseudo-intervention during training to rehearse the extra experts.
Key results
- On CEBaB with T5-MoCE, CLEAR raises post-intervention test task Macro F1 from 81.30 to 82.14 and concept F1 from 89.63 to 91.25 (dev and test values as reported).
- On IMDB-C, CLEAR with T5-MoCE improves post-intervention test task Macro F1 to 80.95 versus 80.63 pre-intervention, while fine-tuning baselines show catastrophic forgetting on dev.
- Ablation shows that activating all experts for all samples lowers performance (e.g., CEBaB task -2.49 points), and CLEAR's metacognitive intervention is only marginally behind the oracle intervention.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply CLEAR instead of the strongest reported baseline, CEMs | T5 backbone · CEBaB test | Macro F1: 81.42 -> 82.14 | Helped |
| Apply CLEAR instead of the strongest reported baseline, CEMs | T5 backbone · IMDB-C test | Macro F1: 80.72 -> 80.95 | Helped |
| Apply CLEAR instead of the strongest reported baseline, CEMs | T5 backbone · ASAP-C test | RMSE: 0.684 -> 0.624 | Helped |
| Add targeted metacognitive intervention instead of disabling intervention (ablation) | CLEAR · CEBaB | Task score: 81.30 -> 81.80 | Helped |
| Add targeted metacognitive intervention instead of disabling intervention (ablation) | CLEAR · IMDB-C | Task score: 80.63 -> 80.95 | Helped |
| Add targeted metacognitive intervention instead of disabling intervention (ablation) | CLEAR · ASAP-C | Task score: 0.694 -> 0.624 | Helped |
| Activate all experts for all samples instead of allocating experts selectively (ablation) | CLEAR · CEBaB | Task score: 81.30 -> 78.81 | Hurt |
| Activate all experts for all samples instead of allocating experts selectively (ablation) | CLEAR · IMDB-C | Task score: 80.63 -> 78.55 | Hurt |
| Activate all experts for all samples instead of allocating experts selectively (ablation) | CLEAR · ASAP-C | Task score: 0.694 -> 0.726 | Hurt |
| Use oracle labels to identify predictions for intervention (ablation) | CLEAR · CEBaB | Task score: 81.30 -> 82.06 | Helped |
| Use oracle labels to identify predictions for intervention (ablation) | CLEAR · IMDB-C | Task score: 80.63 -> 81.36 | Helped |
| Use oracle labels to identify predictions for intervention (ablation) | CLEAR · ASAP-C | Task score: 0.694 -> 0.597 | Helped |
Surprising findings
- Activating all experts for every sample ("max" intervention) degrades performance, attributed to severe overfitting from over-allocated parameters.
- LF-CBMs, which use GPT-4 for noisy concept labels, degrade after intervention on some test metrics.
Limitations
- Backbone choice limited by availability of open-source pretrained MoE models.
- IMDB-C training set is very small (100 examples), and ASAP-C is self-curated with only two annotators.
- Gains from intervention are small (often under 1 point on task F1).
Experimental setup
Datasets: CEBaB IMDB-C ASAP
Benchmarks: CEBaB IMDB-C ASAP-C
Metrics: Macro F1 RMSE MSE
Baselines: GPT-4 prompting fine-tuning ITI Vanilla CBM LF-CBM CEM
Builds on: concept bottleneck models mixture of experts ITI label-free CBM concept embedding models
Closest papers by meaning
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- Decoupling Metacognition from Cognition: A Framework for Quantifying Metacognitive Ability in LLMs (AAAI 2025 · similarity 0.89)
- LLMs Show No Signs Of Individuated Metacognition (NeurIPS 2026 · similarity 0.88)
- Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs (ICLR 2024 · similarity 0.88)
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