Evaluating natural language generation systems is challenging due to the diversity of valid outputs. While human evaluation is the gold standard, it suffers from inconsistencies, lack of standardization, and demographic biases, limiting reproducibility. LLM-based evaluators offer a scalable alternative but are 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.

Recent advances in automatic evaluation of natural language generation have increasingly relied on large language models as general-purpose metrics. While effective, these approaches often require high-capacity models, which introduce substantial computational costs, and remain susceptible to known evaluation pathologies, such as over-reliance on likelihood. We introduce ContrastScore, a contrastive evaluation paradigm that builds on the widely used BARTScore formulation by comparing token-level probabilities between a stronger and a weaker model. Instead of relying on single-model likelihoods or prompt-based judgments, ContrastScore captures disagreement between models to better reflect confidence and uncertainty in generation quality. Empirical results on summarization and machine translation benchmarks show that ContrastScore, instantiated with paired moderate-scale models across both Qwen and LLaMA families, consistently outperforms larger alternatives, such as Qwen 7B and LLaMA 8B, in correlation with human ratings. In addition to improving evaluation quality, ContrastScore significantly reduces susceptibility to likelihood bias, offering a more robust and cost-effective alternative to larger LLM-based evaluation methods.

Automatic evaluation of generated textual content presents an ongoing challenge within the field of NLP. Given the impressive capabilities of modern language models (LMs) across diverse NLP tasks, there is a growing trend to employ these models in creating innovative evaluation metrics for automated assessment of generation tasks. This paper investigates a pivotal question: Do language model-driven evaluation metrics inherently exhibit bias favoring texts generated by the same underlying language model? Specifically, we assess whether prominent LM-based evaluation metrics (e.g. BARTScore, T5Score, and GPTScore) demonstrate a favorable bias toward their respective underlying LMs in the context of summarization tasks. Our findings unveil a latent bias, particularly pronounced when such evaluation metrics are used in a reference-free manner without leveraging gold summaries. These results underscore that assessments provided by generative evaluation models can be influenced by factors beyond the inherent text quality, highlighting the necessity of developing more reliable evaluation protocols in the future.