I’m a research scientist at Anthropic doing empirical safety research on language models. In the past, I’ve worked on automated red teaming of language models [1], the inverse scaling prize [2], learning from human feedback [3][4], and empirically testing debate [5][6], iterated amplification [7], and other methods [8] for scalably supervising AI systems as they become more capable.
Website: https://ethanperez.net/
Are you measuring the average probability the model places on the sycophantic answer, or the % of cases where the probability on the sycophantic answer exceeds the probability of the non-sycophantic answer? In our paper, we did the latter; someone mentioned to me that it looks like the colab you linked does the former (though I haven’t checked myself). If this is correct, I think this could explain the differences between your plots and mine in the paper; if pretrained LLMs are placing more probability on the sycophantic answer, I probably wouldn’t expect them to place that much more probability on the sycophantic than non-sycophantic answer (since cross-entropy loss is mode-covering).
(Cool you’re looking into this!)