RepBench: Compiling Benchmarks into Capability Representations for Large Language Models
Representation engineering reads and steers capability directions in large language models, yet methods are typically evaluated on paper-specific synthetic data. The resulting measurements are difficult to compare or reproduce and may reflect surface patterns rather than capabilities. We present RepBench, a benchmark-grounded data layer for capability-aligned representation probing. Crawling 13,427 benchmark papers yields a taxonomy of 182 capability clusters in 13 families; harvesting 353 public benchmark datasets yields 46,149 audited probe texts covering 94 capabilities, each supported by at least two independent benchmarks. This multi-benchmark design reduces dependence on any single ...
| Authors | Yanshi Li, Xueru Bai, Shuman Liu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 30 July 2026 |
| Last updated at source | 14 August 2026 |
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