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papersTODAY 04:00 UTC

Paper Proposes Calibration Tests for LLM Interpretability Measurements

A new arXiv paper argues that causal claims about the internal workings of large language models depend on measurements such as projections, cosine similarities, ablation deltas, and interchange patches. The authors catalog the specific ways these instruments fail and propose calibration steps to take before relying on their results. The work is listed under arXiv's machine learning and AI categories.

arXivai-researchmechanistic-interpretabilitymodel evaluation

COVERAGE · 3 REPORTS · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.AICalibrating Interpretability Instruments Before Trusting Their Verdicts ↗TODAY 04:00 UTC
arXiv cs.CLCalibrating Interpretability Instruments Before Trusting Their Verdicts ↗TODAY 04:00 UTC
arXiv cs.LGCalibrating Interpretability Instruments Before Trusting Their Verdicts ↗TODAY 04:00 UTC

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