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cs.LG2024
When Can Proxies Improve the Sample Complexity of Preference Learning?
Yuchen Zhu, Daniel Augusto de Souza, Zhengyan Shi +4
We address the problem of reward hacking, where maximising a proxy reward does not necessarily increase the true reward. This is a key concern for Large Language Models (LLMs), as…
cs.LG2024★ 2 cited
An Auditing Test To Detect Behavioral Shift in Language Models
Leo Richter, Xuanli He, Pasquale Minervini +1
As language models (LMs) approach human-level performance, a comprehensive understanding of their behavior becomes crucial. This includes evaluating capabilities, biases, task perf…