NLP Evaluation Practices Reviewed in Comprehensive Taxonomy

Recent advances in large language models (LLMs) have spurred critical evaluations of existing evaluation methodologies in natural language processing (NLP). This scoping review collates insights on evaluation concerns and develops a taxonomy that outlines common positions and trade-offs relevant to these methodologies. The presentation of this work situates modern debates within historical context, offering a comprehensive reference for evaluating practices in NLP.

The implications of this research extend to practitioners in NLP by providing a structured checklist aimed at fostering more intentional evaluation design and interpretation. This development not only enhances the methodological rigor of evaluations but also aids in addressing the nuanced complexities debated within the field. Ultimately, this work strives to improve the overall reliability and validity of NLP evaluations, potentially influencing future framework adaptations.