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Jul 1, 2024

Werewolf Benchmark

Luhan Mikaelson, Zach Nguyen, Andy Liu, Jord Nguyen, Akash Kundu

Details

Details

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In this work we put forward a benchmark to quantitatively measure the level of strategic deception in LLMs using the Werewolf game. We run 6 different setups for a cumulative sum of 500 games with GPT and Claude agents. We demonstrate that state-of-the-art models perform no better than the random baseline. Our findings also show no significant improvement in winning rate with two werewolves instead of one. This demonstrates that the SOTA models are still incapable of collaborative deception.

Cite this work:

@misc {

title={

Werewolf Benchmark

},

author={

Luhan Mikaelson, Zach Nguyen, Andy Liu, Jord Nguyen, Akash Kundu

},

date={

7/1/24

},

organization={Apart Research},

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

howpublished={https://apartresearch.com}

}

Reviewer's Comments

Reviewer's Comments

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Esben Kran

This was a great read and a thorough exploration of the topic. I really enjoy when you see conflicting statistics that you went ahead and did manual investigation of the games as well. Too few in ML benchmarks do this to a proper degree. I would be interested to see comparisons to human baselines as well as more reasoning about how the different categories of deception might map to real-world scenarios. Similarly, I'm curious to see whether we can have highly capable agents in Villager roles in the game that are incapable of any deception.

Possibly fine-tune a model against this deceptive behavior. Of course, that might mean the Villager loses out to deceptive agents and it could be a general comment for real-world applicability that if we construct multi-agent scenarios where all agents are truthful and can trust each other, this would lead to a Villager like that winning due to a Werewolf losing out because of truthfulness.
Would also be interesting to see a Werewolf being completely honest but truthful Villagers not knowing whether they work with other truthful agents, leading to scenarios where they might not trust the other agent despite it saying it's a Werewolf. As far as I understand, that would e.g. cause a Villager to identify a Werewolf as a minion (not 100% sure about the rules).

Great work! Separately, I'd highly recommend you add a PR to the README of the Werewolf-gpt repo to add a heading called “Research” with your piece of research with a very quick summary. Then you can see if the author might be interested in adding it! I can also help with that if you'd like.

Apr 14, 2025

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Jan 24, 2025

Safe ai

The rapid adoption of AI in critical industries like healthcare and legal services has highlighted the urgent need for robust risk mitigation mechanisms. While domain-specific AI agents offer efficiency, they often lack transparency and accountability, raising concerns about safety, reliability, and compliance. The stakes are high, as AI failures in these sectors can lead to catastrophic outcomes, including loss of life, legal repercussions, and significant financial and reputational damage. Current solutions, such as regulatory frameworks and quality assurance protocols, provide only partial protection against the multifaceted risks associated with AI deployment. This situation underscores the necessity for an innovative approach that combines comprehensive risk assessment with financial safeguards to ensure the responsible and secure implementation of AI technologies across high-stakes industries.

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Jan 24, 2025

CoTEP: A Multi-Modal Chain of Thought Evaluation Platform for the Next Generation of SOTA AI Models

As advanced state-of-the-art models like OpenAI's o-1 series, the upcoming o-3 family, Gemini 2.0 Flash Thinking and DeepSeek display increasingly sophisticated chain-of-thought (CoT) capabilities, our safety evaluations have not yet caught up. We propose building a platform that allows us to gather systematic evaluations of AI reasoning processes to create comprehensive safety benchmarks. Our Chain of Thought Evaluation Platform (CoTEP) will help establish standards for assessing AI reasoning and ensure development of more robust, trustworthy AI systems through industry and government collaboration.

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This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.
This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.