Dimension-Level Intent Fidelity Evaluation for Large Language Models: Evidence from Structured Prompt Ablation

2026-05-14 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

A new dimension-level intent fidelity evaluation framework has been developed to assess Large Language Models (LLMs), distinguishing between a model's ability to reproduce structural form and its preservation of specific user intent. This framework was applied in a structured prompt ablation study across 2,880 outputs, encompassing three languages, three task domains, and six LLMs. The study revealed a systematic structural-fidelity split, with 25.7% of Chinese-language outputs and 58.6% of English-language outputs achieving perfect holistic alignment (GA=5) despite exhibiting measurable dimensional intent deficits. Human evaluation confirmed these split-zone outputs represented genuine quality deficits, and dimensional fidelity scores correlated more reliably with human judgments than holistic scores. Further analysis of 2,520 ablation cells characterized model compensation for missing intent, and a weight-perturbation experiment showed severe dimensional inversion is consistently harmful.

Key takeaway

For AI Engineers evaluating LLM performance for user-specific tasks, relying solely on holistic alignment scores is insufficient and can mask significant quality deficits. You should integrate dimension-level intent fidelity evaluation into your assessment pipeline to accurately identify where models fail to preserve specific user intent, even when structural form is correct. This approach will lead to more robust model selection and fine-tuning decisions.

Key insights

Dimension-level intent fidelity evaluation uncovers LLM quality deficits missed by holistic assessment.

Principles

• Holistic scores can mask intent deficits.
• Dimensional fidelity tracks human judgment better.

Method

A structured prompt ablation study across 2,880 outputs, using public-private decomposition and proxy annotation, measures structural recovery and intent fidelity for each semantic dimension.

In practice

• Use dimension-level evaluation for LLM outputs.
• Prioritize intent fidelity in user-specific tasks.

Topics

• LLM Evaluation
• Intent Fidelity
• Structured Prompt Ablation
• Dimensional Fidelity
• Structural-Fidelity Split

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.