15 citations · 16 across the 5 of their papers we have counts for
5 papers
A Benchmark for Scalable Oversight Protocols
Abhimanyu Pallavi Sudhir, Jackson Kaunismaa, Arjun Panickssery
As AI agents surpass human capabilities, scalable oversight -- the problem of effectively supplying human feedback to potentially superhuman AI models -- becomes increasingly criti…
Analyzing Probabilistic Methods for Evaluating Agent Capabilities
Axel Højmark, Govind Pimpale, Arjun Panickssery +2
To mitigate risks from AI systems, we need to assess their capabilities accurately. This is especially difficult in cases where capabilities are only rarely displayed. Phuong et al…
Future Events as Backdoor Triggers: Investigating Temporal Vulnerabilities in LLMs
Sara Price, Arjun Panickssery, Sam Bowman +1
Backdoors are hidden behaviors that are only triggered once an AI system has been deployed. Bad actors looking to create successful backdoors must design them to avoid activation d…
LLM Evaluators Recognize and Favor Their Own Generations
Arjun Panickssery, Samuel R. Bowman, Shi Feng
Self-evaluation using large language models (LLMs) has proven valuable not only in benchmarking but also methods like reward modeling, constitutional AI, and self-refinement. But n…
REBUS: A Robust Evaluation Benchmark of Understanding Symbols
Andrew Gritsevskiy, Arjun Panickssery, Aaron Kirtland +7
We propose a new benchmark evaluating the performance of multimodal large language models on rebus puzzles. The dataset covers 333 original examples of image-based wordplay, cluing…