Beyond Surface Statistics: Robust Conformal Prediction for LLMs via Internal Representations

2023-03-30 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

Layerwise CP is a novel conformal prediction framework for large language models (LLMs) that enhances reliability in question answering, particularly under calibration-deployment mismatch. Unlike traditional methods relying on output-level uncertainty signals (e.g., token probabilities, entropy), Layerwise CP utilizes "Layer-Wise Information" (LI) scores derived from internal model representations. These LI scores quantify how input context reshapes predictive entropy across model layers and are used as nonconformity scores within a standard split conformal pipeline. Experiments across closed-ended (MMLU-Pro, MedMCQA) and open-domain (TriviaQA, CoQA) QA benchmarks, using models like Qwen-2.5-3B-Instruct and LLaMA-3.1-8B-Instruct, demonstrate that Layerwise CP achieves a superior validity-efficiency trade-off. It shows clearer gains under cross-domain shifts, reducing empirical miscoverage rate (EMR) by 17.6% and average prediction set size (APSS) by 16.9% on MMLU-Pro compared to surface-level baselines like SConU-Pro, while maintaining competitive in-domain reliability.

Key takeaway

For AI Engineers deploying LLMs in critical question-answering systems, especially those facing potential domain shifts between calibration and deployment, consider integrating Layerwise CP. This method, by leveraging internal model representations, offers more robust uncertainty quantification and can lead to significantly tighter prediction sets without sacrificing reliability. You should evaluate its performance on your specific cross-domain scenarios to capitalize on its improved validity-efficiency trade-off, potentially reducing the need for extensive re-calibration.

Key insights

Internal LLM representations offer more robust uncertainty signals for conformal prediction, especially under distribution shifts.

Principles

• Internal representations preserve semantic and reliability-relevant structure.
• Layer-wise information can be more informative than final-layer statistics.
• Conformal prediction set size reflects residual uncertainty.

Method

Layerwise CP computes Layer-Wise Information (LI) scores by aggregating how input context reshapes predictive entropy across model depth, then combines these with answer frequency to form a robust nonconformity score for split conformal prediction.

In practice

• Use LI scores for conformal prediction in LLM QA.
• Prioritize internal signals when facing domain shift.
• Aggregate evidence from multiple generations for stability.

Topics

• Conformal Prediction
• Large Language Models
• Internal Representations
• Uncertainty Quantification
• Distribution Shift

Best for: AI Engineer, NLP Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.