From Input Perception to Predictive Insight: Modeling Model Blind Spots Before They Become Errors

2025-09-24

AI Summary

Language models frequently fail due to misinterpretation of the initial input—particularly idiomatic, metaphorical, or context-sensitive expressions—rather than errors in output generation. This paper proposes a lightweight, input-only method for pre-emptively detecting such failures. It leverages token-level likelihood features derived from surprisal and information density hypotheses, jointly modeling span-localized uncertainty and global statistical patterns—without requiring access to model internals or generated outputs. The approach is model-scale adaptive: larger models rely more on local features, while smaller models emphasize global patterns. Evaluated on five challenging language understanding benchmarks, it significantly outperforms strong baselines, demonstrating effectiveness, cross-model generalizability, and computational efficiency.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Language models often struggle with idiomatic, figurative, or context-sensitive inputs, not because they produce flawed outputs, but because they misinterpret the input from the outset. We propose an input-only method for anticipating such failures using token-level likelihood features inspired by surprisal and the Uniform Information Density hypothesis. These features capture localized uncertainty in input comprehension and outperform standard baselines across five linguistically challenging datasets. We show that span-localized features improve error detection for larger models, while smaller models benefit from global patterns. Our method requires no access to outputs or hidden activations, offering a lightweight and generalizable approach to pre-generation error prediction.

Problem

• Predicting language model failures on idiomatic and figurative inputs
• Detecting input misinterpretations before generating flawed outputs
• Developing input-only methods for pre-generation error anticipation

Innovation

• Input-only failure prediction using token likelihood
• Localized uncertainty features outperform standard baselines
• Lightweight method requiring no output access