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arXiv · 2511.22312

Token-Level Marginalization for Multi-Label LLM Classifiers

Anjaneya Praharaj·Jaykumar Kasundra
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Abstract

This paper addresses the critical challenge of deriving interpretable confidence scores from generative language models (LLMs) when applied to multi-label content safety classification. While models like LLaMA Guard are effective for identifying unsafe content and its categories, their generative architecture inherently lacks direct class-level probabilities, which hinders model confidence assessment and performance interpretation. This limitation complicates the setting of dynamic thresholds for content moderation and impedes fine-grained error analysis. This research proposes and evaluates three novel token-level probability estimation approaches to bridge this gap. The aim is to enhance model interpretability and accuracy, and evaluate the generalizability of this framework across different instruction-tuned models. Through extensive experimentation on a synthetically generated, rigorously annotated dataset, it is demonstrated that leveraging token logits significantly improves the interpretability and reliability of generative classifiers, enabling more nuanced content safety moderation.

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Anjaneya Praharaj, Jaykumar Kasundra. 2025-11-27. Token-Level Marginalization for Multi-Label LLM Classifiers. https://arxiv.org/abs/2511.22312

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