Challenge: Performance-based evaluation has been at the expense of other attributes valued by the NLP community, such as compactness and energy efficiency.
Approach: They propose to frame both the leaderboard and NLP practitioners as consumers and the benefit they get from a model as its utility to them.
Outcome: The proposed model size and energy efficiency benchmarks have been successful in driving the creation of more accurate models, but have been at the expense of other attributes valued by the NLP community.

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Evaluation Examples are not Equally Informative: How should that change NLP Leaderboards? (2021.acl-long)

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Challenge: Rather than replacing leaderboards, we advocate a re-imagining of the model to highlight if and where progress is made.
Approach: They propose a Bayesian leaderboard model where latent subject skill and latent item difficulty predict correct responses.
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ExplainaBoard: An Explainable Leaderboard for NLP (2021.acl-demo)

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Challenge: Using leaderboards, researchers can track the performance of various systems on various NLP tasks.
Approach: They propose a new conceptualization and implementation of NLP evaluation using a leaderboard.
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Confidence and Stability of Global and Pairwise Scores in NLP Evaluation (2025.acl-srw)

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Challenge: Modern natural language processing benchmarks are often represented as pairwise comparison leaderboards, such as LMSYS Arena.
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With Little Power Comes Great Responsibility (2020.emnlp-main)

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Challenge: Underpowered experiments make it more difficult to discern the difference between statistical noise and meaningful model improvements and increase the chances of exaggerated findings.
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Academics Can Contribute to Domain-Specialized Language Models (2024.emnlp-main)

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Challenge: Commercially available models dominate academic leaderboards, focusing on creating and adapting general-purpose models . however, general- purpose models often underperform in specialized domains, and domain-specific models yield superior results.
Approach: They advocate for a renewed focus on developing and evaluating domain- and task-specific models . they advocate for an adapted or adapted model that can be used to improve academic leaderboard standings .
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Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020.acl-main)

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Challenge: 146 papers analyzing "bias" in NLP systems lack normative reasoning, we find . authors propose three recommendations for work analyzing “bias” in Nlp systems .
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How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact (2021.findings-acl)

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Challenge: Recent years have seen many breakthroughs in natural language processing (NLP), transitioning it from a mostly theoretical field to one with many real-world applications.
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What happens if you treat ordinal ratings as interval data? Human evaluations in NLP are even more under-powered than you think (2021.emnlp-main)

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Challenge: Existing studies have shown that human evaluations in NLP are under-powered because of two common factors: they treat ordinal data as interval data and operate under high variance settings.
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When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards (2024.acl-long)

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Challenge: Existing leaderboards are often taken at face value, but this is costly . a recent study shows that minor perturbations to the benchmark result in rankings up to 8 positions.
Approach: They propose to use a *hybrid* scoring method for answer selection for large language models . they find that minor perturbations to the benchmark result in rankings changes .
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Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand (2022.naacl-main)

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Challenge: Recent advances on models and metrics should benefit and inform each other, authors argue . bidimensional leaderboards allow for fast, accurate evaluation of language generation models .
Approach: They propose a bidimensional leaderboard that tracks progress in language generation models and metrics for their evaluation.
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