There’s plenty of evidence for political bias in LLMs, but very few evals reflect realistic LLM use cases — which is where bias actually matters. IssueBench, our attempt to fix this, is accepted at TACL, and I will be at #EMNLP2025 next week to talk about it! New results 🧵

LLMs are good at simulating human behaviours, but they are not going to be great unless we train them to. We hope SimBench can be the foundation for more specialised development of LLM simulators. I really enjoyed working on this with @tiancheng.bsky.social et al. Many fun results 👇

🏆 Thrilled to share that our HateDay paper has received an Outstanding Paper Award at #ACL2025 Big thanks to my wonderful co-authors: @deeliu97.bsky.social, Niyati, @computermacgyver.bsky.social, Sam, Victor, and @paul-rottger.bsky.social! Thread 👇and data avail at huggingface.co/datasets/man...

Very excited about all these papers on sociotechnical alignment & the societal impacts of AI at #ACL2025. As is now tradition, I made some timetables to help me find my way around. Sharing here in case others find them useful too :) 🧵

Can LLMs learn to simulate individuals' judgments based on their demographics? Not quite! In our new paper, we found that LLMs do not learn information about demographics, but instead learn individual annotators' patterns based on unique combinations of attributes! 🧵

📈Out today in @PNASNews!📈 In a large pre-registered experiment (n=25,982), we find evidence that scaling the size of LLMs yields sharply diminishing persuasive returns for static political messages.  🧵:

Are LLMs biased when they write about political issues? We just released IssueBench – the largest, most realistic benchmark of its kind – to answer this question more robustly than ever before. Long 🧵with spicy results 👇

I’m thrilled to share that our paper on mitigating false refusal in language models has been accepted to ICLR 2025 @iclr-conf.bsky.social! arxiv.org/abs/2410.03415 Joint work with chengzhi, @paul-rottger.bsky.social, @barbaraplank.bsky.social.

Today, we are releasing MSTS, a new Multimodal Safety Test Suite for vision-language models! MSTS is exciting because it tests for safety risks *created by multimodality*. Each prompt consists of a text + image that *only in combination* reveal their full unsafe meaning. 🧵