Jul 1, 2024

Sandbag Detection through Model Degradation

Cam Tice, Philipp Alexander Kreer, Fedor Ryzhenkov, Nathan Helm-Burger, Prithviraj Singh Shahan

🏆 1st place by peer review

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Details

Summary

We propose a novel technique to detect sandbagging in LLMs by adding varying amount of noise to model weights and monitoring performance.

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Cite this work:

@misc {

title={

Sandbag Detection through Model Degradation

author={

Cam Tice, Philipp Alexander Kreer, Fedor Ryzhenkov, Nathan Helm-Burger, Prithviraj Singh Shahan

date={

7/1/24

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

Review

Reviewer Name

Constructive critique

Criteria 1:

Advancing interpretability (Possible questions to answer in this criteria: Does the project contribute to the field of mechanistic interpretability? Does it provide new insights into understanding or steering AI model behavior? How well does it move us towards reprogramming AI models? How original and innovative is the approach?)

2.5

Criteria 2:

Relevance for AI safety (Possible questions to answer in this criteria: How important is the contribution to advancing the field of AI safety? Do we expect the results to generalize beyond the specific case(s) presented in the submission? Does the approach introduce new safety mechanisms or enhance existing ones in innovative ways?))

2.5

Criteria 3:

Methodology & presentation (Possible questions to answer in this criteria: How well is the project executed from a technical standpoint?, Is the code well-structured, documented, and reproducible?, How effectively does it utilize Goodfire's SDK/API and other provided resources?, How clearly and effectively is the research presented in the paper?, Quality of visualizations and demos (if applicable), Clarity of methodology explanation and results interpretation))

2.5

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