May 27, 2024

Benchmarking Dark Patterns in LLMs

Jord Nguyen, Akash Kundu, Sami Jawhar

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Summary

This paper builds upon the research in Seemingly Human: Dark Patterns in ChatGPT (Park et al, 2024), by introducing a new benchmark of 392 questions designed to elicit dark pattern behaviours in language models. We ran this benchmark on GPT-4 Turbo and Claude 3 Sonnet, and had them self-evaluate and cross-evaluate the responses

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@misc {

title={

Benchmarking Dark Patterns in LLMs

author={

Jord Nguyen, Akash Kundu, Sami Jawhar

date={

5/27/24

organization={Apart Research},

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

howpublished={https://apartresearch.com}

}

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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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