Trojan Detection Challenge 2023 (LLM Edition)
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About
The Trojan Detection Challenge 2023 (LLM Edition) is a NeurIPS 2023 competition focused on advancing methods for detecting hidden functionality in large language models (LLMs). The competition features two tracks: Trojan Detection and Red Teaming. The Trojan Detection Track challenges participants to identify triggers for hidden behaviors (trojans) in LLMs. The Red Teaming Track tasks participants with developing automated methods to elicit specific undesirable behaviors. There's a $30,000 prize pool, and winning teams will be invited to co-author a publication and present at a NeurIPS workshop. The competition uses open-source LLMs (Pythia and Llama-2-chat) and encourages participants to share their methods.
Platform
Task
Features
• red teaming
• competition
• llm safety
• trojan detection
FAQs
What are the current rules?
The rules are available here: [Here](index.html#rules).
Can the organizers change the rules?
Yes, with participant consent if urgently needed.
How do I contact the organizers?
Contact us at tdc2023-organizers@googlegroups.com
Who can participate in the competition?
The competition is open to the public.
When is the deadline to register?
You can register anytime during the competition.
How many people can I have in my team?
Teams can have any number of members. Solo teams are allowed.
Where can I download data and submit results?
See the [Getting Started](start.html) page.
How many submissions can each team enter per competition track?
5 submissions per day during validation, 5 total in the test phase. Only one account per team.
Are participants required to share the details of their method?
We encourage sharing; winning teams must share with organizers.
What are the details for the Trojan Detection Track?
Details are here: [Here](tracks.html#trojan-detection).
What are the details for the Red Teaming Track?
Details are here: [Here](tracks.html#red-teaming).
Why are you using the baselines you have chosen?
The baselines are well-known text optimization and red teaming methods from the academic literature.
Why are you using the LLMs you have chosen?
For Trojan Detection, open-source Pythia LLMs are used for broader participation; Llama-2-chat for Red Teaming due to robustness.
Why are you using the particular trojan attack you have chosen?
We use the simplest trojan attack for its resemblance to the red teaming task, fostering connections between communities.
Is it "trojans" or "Trojans"?
Both are used in the literature; "trojans" is used for better flow.
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