🤖 AI Summary
This work addresses the limitations of existing representation engineering approaches, which rely on synthetic data and suffer from irreproducible evaluations and susceptibility to superficial patterns. The authors construct the first large-scale, multi-source aligned capability representation framework grounded in real-world benchmarks, curated from over 10,000 academic papers and hundreds of public datasets, spanning 94 distinct capabilities. This framework enables cross-benchmark aggregation of capability vectors and transferable evaluation, effectively mitigating bias from any single data source. Experiments across 12 large language models reveal that benchmark-pooled capability vectors exhibit stable clustering structures; differential mean achieves the best performance in 10 models, while logistic regression outperforms others across the greatest number of capability–model combinations, underscoring the critical influence of both evaluation dimensions and readout methodologies.
📝 Abstract
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 source: raw per-text vectors exhibit no natural cluster granularity, whereas benchmark-pooled capability vectors show an interior clustering optimum at a small number of clusters on all 12 evaluated models, with low agreement to the human taxonomy. Under cross-benchmark transfer evaluation across twelve models completed by all four readouts, difference-in-means attains the highest model-level mean on ten models, while logistic regression wins the most capability-model cells. This disagreement shows that the readout method and aggregation criterion are meaningful evaluation dimensions. The pipeline, corpus, and evaluation code are released as a reusable closed-loop workflow.