Super important paper and what a nice interdisciplinary group of co authors!!! 😁

Curious about my PhD research?
▶️ Watch a 10-min talk + my defense: lnkd.in/ej_MWDtt
📘 Read the dissertation: lnkd.in/efBW97WB
📰 Or read the short news article: lnkd.in/eizZg5VN

Amazing co-authors broadened my perspective and made me a better scientist. Thank you so much for that! 🙏

Also to my doctoral committee: @damiantrilling.net , Annette Hautli-Janisz, reshmi G Pillai, @Khalid Al Khatib & Antal van den Bosch: thank you for your thoughtful (and fun!) questions.

And huge thanks to my incredible paranymphs @urjakh.bsky.social and Selene Baez Santamaria 👯‍♀️. From Zoom rooms to the stage, our journey has been full of growth, laughter, and mutual support. ❤️

In fact, all PhDs from @cltl.bsky.social were a great community of support. 💖

But then working as a (university) researcher also comes with a lot of downsides, including insecurity and pressure in random “which grant or paper wins” arenas which I do not vibe well with.

But what then? What do?

Btw I’m serious about this career change comment.

I’m having a sort of post-PhD career reflection where I realize that these kind of things don’t spark joy for me but seem to be a big part of being an AI dev in industry.

I mean, I have heard people say they enjoy the puzzling aspect and the feeling accomplished when they fix it.

Personally, for me that never weights up against the annoyance and what feels like endless wasted time.

Also, I realize some people really love the “puzzle” aspect but I don’t like these kind of puzzles. It makes me stressed and annoyed. Maybe I should find another field to work in. 😛

I also really hate it when people who do not work in NLP/LLMs then say “oh no but with conda and a requirements.txt it’s easy, right?”, not realizing the morass of ever-new models and architectures I live in.

While I am not at #NAACL, I gave a talk about this paper (and more work in my dissertation) last Friday at @annarogers.bsky.social ’s lab, very nice discussion there! 😃

Paper: lnkd.in/eBBSi6_p
Code: lnkd.in/ezwRGpjP
Slides: lnkd.in/erPP5fpV

Want to know more? Message me!

💡We find that:
- Experts use 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 to assess the LLM;
- Surprisingly, 𝗹𝗼𝗻𝗴𝗲𝗿 𝗮𝗻𝗱 𝗺𝗼𝗿𝗲 𝗻𝘂𝗮𝗻𝗰𝗲𝗱 𝗱𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻𝘀 𝗼𝗳 𝘀𝗲𝘅𝗶𝘀𝗺 developed via LLM-human collaboration;
- Some experts improve zero-shot performance with their improved definition.

#NLProc #CSS #computationalsocialscience

Our study consisted of four components:

1) a survey of sexism researchers
two interactive experiments on expert-LLM interactions; 2). assessing the LLM;
3). co-creating of sexism definitions with the LLM;
4) using these definitions in zero-shot detection with LLMs on five sexism datasets: 👩‍🔬 + 🤖

This work was the outcome of my Junior Research Visit grant at @gesis.org last year, and is the final chapter of my dissertation! 🤩

Our method allowed us to measure connections between experts, sexism definition, dataset, & classification performance in zero-shot sexism classification. 🔍🔬