Using Large Language Models (LLMs) for Information Extraction (IE) inherently involves transforming diverse textual inputs into strictly formatted outputs. Guaranteeing this structural validity introduces a hidden energy tax driven by constrained decoding mechanisms, serialisation formats, and underlying serving configurations. Consequently, standard token-level metrics fail to capture the true energy cost of generative IE, which also cannot be predicted by model size alone. To address this, we introduce the first dedicated benchmark evaluating the energy-accuracy trade-offs of generative IE. Our framework establishes task-aligned energy metrics, including Energy per True Positive (J/TP) and the F1-Energy Ratio (F₁/kJ). Through comprehensive ablation across architectures, decoding regimes, and serving-layer limits, we demonstrate that efficiency is governed by complex parameter interactions. We translate these findings into actionable configuration guidelines, equipping practitioners to maximise predictive accuracy, optimise energy yield, or achieve a sustainable Pareto balance.

Cross-lingual Named Entity Recognition (NER) leverages knowledge transfer between languages to identify and classify named entities, making it particularly useful for low-resource languages. We show that the data-based cross-lingual transfer method is an effective technique for cross-lingual NER and can outperform multi-lingual language models for low-resource languages. This paper introduces two key enhancements to the annotation projection step in cross-lingual NER for low-resource languages. First, we explore refining word alignments using back-translation to improve accuracy. Second, we present a novel formalized projection approach of matching source entities with extracted target candidates. Through extensive experiments on two datasets spanning 57 languages, we demonstrated that our approach surpasses existing projection-based methods in low-resource settings. These findings highlight the robustness of projection-based data transfer as an alternative to model-based methods for cross-lingual named entity recognition in low-resource languages.