Beyond One-Size-Fits-All: Inversion Learning for Highly Effective NLG
  Evaluation Prompts
Authors: name: Hanhua Hong, name: Chenghao Xiao, name: Yang Wang, name: Yiqi Liu, name: Wenge Rong, name: Chenghua Lin

Evaluating natural language generation (NLG) systems is challenging due to
the diversity of valid outputs. While human evaluation is the gold standard, it
suffers from inconsistencies, lack of standardisation, and demographic biases,
limiting reproducibility. LLM-based evaluation offers a scalable alternative
but is highly sensitive to prompt design, where small variations can lead to
significant discrepancies. In this work, we propose an inversion learning
method that learns effective reverse mappings from model outputs back to their
input instructions, enabling the automatic generation of highly effective,
model-specific evaluation prompts. Our method requires only a single evaluation
sample and eliminates the need for time-consuming manual prompt engineering,
thereby improving both efficiency and robustness. Our work contributes toward a
new direction for more robust and efficient LLM-based evaluation.