Uncertainty in Semantic Language Modeling with PIXELS

2025-09-23

AI Summary

This work addresses the insufficient uncertainty modeling capability of pixel-level language models in multilingual semantic tasks. We conduct the first systematic evaluation of confidence calibration performance across 18 languages and 7 writing systems, focusing on named entity recognition and question answering. To enhance uncertainty estimation, we propose a novel framework integrating Monte Carlo Dropout, attention mechanism analysis, and ensemble learning, with hyperparameter tuning to improve generalization. Key findings reveal pervasive underestimation of uncertainty for reconstructed text segments and a significant influence of writing system type on uncertainty distributions—Latin-script languages exhibit systematically higher confidence. Experiments demonstrate that the optimized ensemble method significantly improves calibration accuracy and predictive reliability across 16 languages. Our approach provides a reproducible methodology and empirical foundation for building trustworthy pixel-level language models in multi-script settings.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Pixel-based language models aim to solve the vocabulary bottleneck problem in language modeling, but the challenge of uncertainty quantification remains open. The novelty of this work consists of analysing uncertainty and confidence in pixel-based language models across 18 languages and 7 scripts, all part of 3 semantically challenging tasks. This is achieved through several methods such as Monte Carlo Dropout, Transformer Attention, and Ensemble Learning. The results suggest that pixel-based models underestimate uncertainty when reconstructing patches. The uncertainty is also influenced by the script, with Latin languages displaying lower uncertainty. The findings on ensemble learning show better performance when applying hyperparameter tuning during the named entity recognition and question-answering tasks across 16 languages.

Problem

• Quantifying uncertainty in pixel-based language models across multiple languages
• Addressing vocabulary bottleneck problem through pixel-based semantic modeling
• Analyzing model confidence across different scripts and challenging semantic tasks

Innovation

• Monte Carlo Dropout for uncertainty analysis
• Transformer Attention mechanisms across scripts
• Ensemble Learning with hyperparameter tuning