A new very creative method for measuring ambiguity with LLMs co-led by @dsallydt and @LukeThorburn_ (EMNLP Oral 2026). When I worked with @mhtessler @summerfieldlab et al on the Habermas Machine (a project in which we use LLMs to find statements that maximize agreement within groups of people), we often found that LLMs maximize consensus by simply making statements more ambiguous. Eg a statement like "peaceful protests should be allowed" will likely lead to high agreement but that's because a lot of disagreement hides in different interpretations of what's "peaceful". One way to measure ambiguity is to ask lots of people how they would interpret a statement and see if those people disagree. But this is obviously impossible to do at scale. In this work we show that we can use LLMs to help approximate this. Read Sally's thread or the paper for more details. I think work like this is super important, for example to stress tests AI constitutions and model specs to see which rules are ambigious and in which situations. If you are in Budapest, please stop by Sally's presentation.