Dynamic Adversarial Data Collection Workshop @DADCworkshop
The First Workshop on Dynamic Adversarial Data Collection (DADC), co-located with NAACL 2022: https://t.co/toZdHMYfv2dadcworkshop.github.io Seattle, WashingtonJoined April 2022
I really enjoyed my @MLStreetTalk chat with Tim at #NeurIPS2024 about some of the research we've been doing on reasoning, robustness and human feedback. If you have an hour to spare and are interested in some semi-coherent thoughts revolving around AI robustness, it may be worth…
We are honoured to have the amazing @VeredShwartz giving a talk at our UCL NLP Meetup titled "Incorporating Commonsense Reasoning into NLP Models"! Join us later today, Friday 29th of July, at 4:30pm BST: meetup.com/ucl-natural-la…
The First Workshop on Dynamic Adversarial Data Collection (DADC) @DADCworkshop , co-located with NAACL 2022: (lnkd.in/ghvAmJJd)
This paper lnkd.in/gtwfQBrE analyses the Dynamic Adversarial Data Collection protocol on the annotator’s experience and the data quality.
This workshop would not have been possible without all the incredible authors who submitted their work, including @zhansheng , @ruixiangcui & @sintelion 🙌 Here's some their awesome posters 🤩🤩🤩
I know I am biased, but how cool are the @DADCworkshop stickers??🤩 Come join us in room Duc Sol to claim yours, as we are still here with presentations of our winners on the Shared Task!🙌🏻 #DADC22#NAACL2022
Talking of the #DADC Community 🤝....Our social is tonight from 6:30-9:30pm @ a rooftop located on 1823 Minor Ave, Seattle (5 min walk from conference venue). There will be 🍕 and 🍹🧃 Spread the word 🗣️ + pls fill in the poll so we can get a sense of numbers 📊
Our final session of the day focuses on the **The Future of the DADC Community** 🔮We kick off with our #SharedTask on better data and better models. Congrats to all participants 🎉🎉 including our presenters for today #TeamFireworks 🎆 #TeamLonghorns 🐂 #TeamSuperSamplers🎲
#PanelPOVs@tongshuangwu's summary: "We don't know a lot of things....😲 so we need to run pilot studies, just keep iterating and not be afraid to make mistakes"
#PanelPOVs@laroyo's summary: "We don't understand diversity yet. We need to focus more on experiments which tell us what diversity is needed to deal with good + bad bias"
#PanelPOVs@annargrs's summary: "at least in the past, training models from scratch, we knew what we were getting. Now with PT language models, we're stripped back to prompt engineering"
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