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

2026-05-15 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · 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 involving 2,880 outputs across three languages, three task domains, and six LLMs. The study revealed a systematic structural-fidelity split: 25.7% of Chinese-language outputs with perfect holistic alignment (GA=5) still showed measurable dimensional intent deficits, a proportion that increased to 58.6% for English-language outputs. Human evaluation validated these "split-zone" outputs as genuine quality deficits, confirming that dimensional fidelity scores more reliably track human judgments than holistic scores. Further analysis of 2,520 ablation cells characterized when models compensate for missing intent and when they fail, with a weight-perturbation experiment showing that moderate misalignment is absorbed, but severe dimensional inversion is consistently harmful.

Key takeaway

For AI Engineers evaluating LLM outputs for user-specific tasks, relying solely on holistic alignment scores (e.g., GA=5) is insufficient and can mask critical intent fidelity deficits. You should integrate dimension-level intent fidelity evaluation into your assessment pipeline to accurately capture how well LLMs preserve specific user intent, especially for English-language applications where hidden deficits are more prevalent. This approach will provide a more reliable measure of output quality aligned with human judgment.

Key insights

Holistic LLM evaluation scores can mask significant dimensional intent fidelity deficits, especially in English.

Principles

• Structural recovery does not guarantee intent preservation.
• Dimensional fidelity tracks human judgment better.
• Severe dimensional inversion is consistently harmful.

Method

The proposed framework uses structured prompt ablation to separately measure structural recovery and intent fidelity for each semantic dimension, complemented by human evaluation.

In practice

• Evaluate LLMs with dimension-level intent fidelity.
• Focus on English outputs for potential hidden deficits.

Topics

• Dimension-Level Intent Fidelity
• Large Language Model Evaluation
• Structured Prompt Ablation
• Holistic Alignment Scores
• Intent Preservation

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 cs.CL updates on arXiv.org.