The scene where she appears is the best scene in the film imo
www.youtube.com/watch?v=VfkQ...

Please reach out if you'd like to chat - I'm open to new collaborations as a postdoc (in 2 weeks!). I'm still into fairness/reference/reasoning, but also want to do more interpretability work, and start on some new directions (linguistic acceptability/plausibility and memorization/generalization).

At COLM I'm co-presenting a meta-evaluation of LLM misgendering (led by @arjunsubgraph.bsky.social), ongoing work on using decoder-only models to simulate partial differential equations (led by @palomagherreros.bsky.social), and I'm co-organizing the @interplay-workshop.bsky.social

In this other paper I look at the effects of LLM architecture on pronoun predictions after explicitly showing the right coreference, but the effects of RLHF and other post-training is an interesting question and to my knowledge unstudied!
direct.mit.edu/tacl/article...

Yesss we made a new, harder version of Winogender Schemas that balances for grammatical case and fixes typos and stuff in the original dataset and we found that case dramatically affects performance! This is at a small scale though
aclanthology.org/2024.crac-1.6/

Our main finding is that across languages, intersectional country-and-gender biases persist even when there appears to be parity along a single axis (just country or just gender), which is why we get—as our title says—Colombian waitresses and Canadian judges. Enjoy Vienna! Here are my highlights.

Thus, going forward, we recommend that future work: (1) Use the evaluation that is appropriate to the final deployment. (2) Take a holistic view of misgendering. (3) Recognize that misgendering is contextual. (4) Center those most impacted by misgendering in system design and evaluation.

By annotating 2400 model generations, we also show that misgendering is complex and goes far beyond pronouns, which is all that automatic metrics currently capture. E.g., models frequently avoid generating pronouns and generate extraneous gendered language, which can be seen as misgendering.

In sum, while both evaluation methods have their time and place, their divergence reflects that they are not substitutes for each other. In the context of misgendering, invalid measurements can lead to poor model selection, deployments, or public misinformation about performance, causing real harms.

We find that overall, probability and generation-based evaluation results disagree with each other (i.e., one shows misgendering, and the other doesn't) on roughly 20% of instances. Check out the preprint for more instance-level, dataset-level, and model-level disagreement metrics.

We transform existing misgendering evaluation datasets into parallel versions for probability- and generation-based evaluation, and then we systematically compare these parallel evaluations across: 4 pronoun sets (he, she, they, xe) and 6 models from 3 families.

We ask: Do the results of generation-based and probability-based evaluations correspond with or diverge from each other? This is important given that LLMs can be used in different ways, sometimes for ranking existing sequences, and sometimes for generation, as with chat-based assistants.

Prior papers (including my own work) have proposed automatic methods for evaluating LLMs for misgendering: Probability-based evaluations use a cloze-style setup with a constrained set of pronouns while generation-based evaluations quantify correct gendering in open-ended generations.

Many popular LLMs fail to refer to individuals with the correct pronouns, which is a form of misgendering. Respecting a person’s social gender is important, and correctly gendering trans individuals, in particular, prevents psychological distress.

I'm discussing it with the other co-organizers and we'll get back to you ASAP!

fierce predator

nerd