arXiv:2602.02898v3 Announce Type: replace Abstract: Language model benchmarks are pervasive and computationally-efficient proxies for real-world performance. However, many recent works find that benchmarks often fail to predict real utility. Towards bridging this gap, we introduce benchmark alignment, where we use limited amounts of information about model performance to automatically update offline benchmarks, aiming to produce new static benchmarks that predict model pairwise preferences in given test settings. We then propose BenchAlign, the first solution to this problem, which learns pref
The proliferation of advanced language models and their increasing real-world application is highlighting the limitations of current benchmark metrics, thus driving the need for more accurate evaluation methodologies.
Improving how we evaluate language models directly impacts their development, deployment, and trustworthiness, which is crucial for their integration into critical applications and the broader economy.
The proposed 'benchmark alignment' using pairwise preferences suggests a more nuanced and potentially reliable method for assessing language model performance beyond current static benchmarks.
- · AI evaluation companies
- · Developers of foundational language models
- · Enterprises deploying LMs for critical tasks
- · AI researchers
- · Developers relying solely on outdated benchmarks
- · Organizations using LMs without robust evaluation
More accurate evaluations will lead to better-performing and more reliable language models in real-world scenarios.
Heightened competition among AI developers as performance comparisons become more precise and reflective of actual utility.
Accelerated adoption of AI agents and complex AI applications due to increased trust in model capabilities and reduced deployment risk.
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Read at arXiv cs.AI