Evaluation Revisited: A Taxonomy of Evaluation Concerns in Natural Language Processing
Ruchira Dhar, Anders S{\o}gaard
Why It Matters
What makes this one worth your time
Understanding and improving evaluation practices is crucial for the development and validation of NLP models, ensuring that they are assessed accurately and fairly.
The paper offers a taxonomy and checklist to improve evaluation practices in NLP.
Summary
The paper conducts a scoping review of evaluation concerns in NLP, developing a taxonomy that synthesizes recurring positions and trade-offs, and proposes a checklist for evaluation design and interpretation.
Key contributions
- Development of a taxonomy of evaluation concerns in NLP.
- Proposal of a structured checklist for evaluation design and interpretation.
Notable insights
- The paper situates contemporary evaluation debates within their historical context, providing a consolidated reference for reasoning about evaluation practices.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2604.25923v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have prompted a growing body of work that questions the methodology of prevailing evaluation practices. However, many such critiques have already been extensively debated in natural language processing (NLP): a field with a long history of methodological reflection on evaluation. We conduct a scoping review of research on evaluation concerns in NLP and develop a taxonomy, synthesizing recurring positions and trade-offs within each area. We also discuss practical implications of the taxonomy, including a structured checklist to support more deliberate evaluation design and interpretation. By situating contemporary debates within their historical context, this work provides a consolidated reference for reasoning about evaluation practices.