Asst. Prof. of Natural Language Processing @UofHaifa
Joined November 2018
- Tweets206
- Following249
- Followers333
- Likes553
בינתיים איך הממשלה ומשרד החינוך דואגים לפריפריה? מבטלים הסעות ילדים לבתי ספר כי הקווים האלה לא משתלמים. הילדים הפריפריאליים שלי כנראה לא צריכים ללמוד בשישי או לעשות יום ארוך….
Rotem Dror retweeted
Attention #NLProc researchers, the EACL 2027 website is officially LIVE: 2027.eacl.org/! 🎉
🇬🇷 Join us in Athens, Greece (Mar 9-13, 2027) at #EACL2027
📅 ARR submission deadline: Aug 6, 2026. Open to all areas of CL/NLP + related fields. Stay tuned for the detailed CfP!
ALT The Acropolis in Athens, Greece
Rotem Dror retweeted
[1/7] Why do frontier LLMs make factual errors?
Is it because they never learned the fact…
or because they can’t access knowledge they already encoded?
In our new paper, we show:
The bottleneck is not encoding; it is recall. 🧵👇
Paper: arxiv.org/abs/2602.14080
Many thanks to @_galyo @bd_eyal @zorikgekhman @eran_ofek59358 @GoogleResearch
Rotem Dror retweeted
A strange trend I've noticed at #ACL2025 is that people are hesitant to reach out to papers/"academic products" authors.
This is unfortunate for both parties! A simple email can save a lot of time to the sender, but is also one of my favorite kind of email as the receiver!
Tomorrow morning (9AM🌅) I'll be giving a keynote talk at LAW (linguistic annotation workshop) at #ACL2025 on annotations in the era of LLMs - see you there!!🌟🌟
Rotem Dror retweeted
The Alternative Annotator Test (alt-test) is a new statistical procedure proposed in our ACL 2025 paper! 🇦🇹🇦🇹 @DrorRotem @roireichart
The goal? To help justify using LLMs over humans. If the LLM passes the test, its annotations can be trusted 😎
arxiv.org/abs/2501.10970
Rotem Dror retweeted
Everyone uses LLMs to annotate data or evaluate models in their research.
But how can we convince others (readers, collaborators, reviewers!!!) that LLMs are reliable? 🤖
Here’s a simple (and low-effort) solution: show the LLM is a *comparable alternative annotator* ✅
Rotem Dror retweeted
🔥הסקירות ממשיכות לזרום ל-X🔥
🧵
המאמר היומי של מייק: 25.06.25
The Alternative Annotator Test for LLM-as-a-Judge: How to Statistically Justify Replacing Human Annotators with LLMs
מאמר 🇮🇱
תפנית מעניינת מתרחשת בתקופה האחרונה בעולם של הערכת ביצועי מודלים. אנחנו כבר לא שואלים רק עד כמה המודל מצליח במבחן כלשהו, אלא שאלה מהותית יותר: האם ניתן לסמוך על מודל שפה שיחליף מתייג אנושי? זו לא שאלה שמדדים מסורתיים כמו דיוק, F1 או הסכמה בין מתייגים יכולים לענות עליה כראוי. תחת זאת, המאמר שנסקור היום מציג שיטה מבוססת סטטיסטיקה לפתרון ביעה זו. בלב המאמר עומדת קריאה להתרחק ממדדי התאמה שטחיים, ולעבור לנימוקים מבוססי השערות סטטיסטיות וניתוח עלות-תועלת.
Rotem Dror retweeted
Preferences drive modern LLM research and development: from model alignment to evaluation.
But how well do we understand them?
Excited to share our new preprint:
Multi-domain Explainability of Preferences
arxiv.org/abs/2505.20088
@roireichart @LiatEinDor
🧵👇
1/11
This is your chance to uncover revolutionary research, forge invaluable connections, and become part of the AI innovation wave.
Conference dates: May 25-27, 2025 📷 Information and registration: lnkd.in/dKqAtP6p
Keynote speakers:
. Hod Lipson, Mechanical Engineering, Columbia University, USA
Prof. Mor Naaman, Information, Cornell Tech, USA
Prof. Tanya Berger-Wolf, Computer Science & Engineering, Ohio State University, USA
Explore AI's transformative power across diverse fields, including robotics, biomedicine, environmental science, social sciences, and the humanities.
Witness cutting-edge research by the University of Haifa's brightest minds, including pioneering work by our researchers and graduate students.
Be inspired by world-renowned keynote speakers and gain practical knowledge from leading industry talks.
Join us at the University of Haifa for the HiAI Conference, a dynamic event at the forefront of Artificial Intelligence.
Immerse yourself in groundbreaking discoveries, gain exclusive insights into the latest advancements, and navigate the future of AI with leading experts.
what load is that? Review load or AC load? or to be more clear - if I'm an AC and I submitted a paper, am I expected to be the AC of at least 4 papers, or, in addition to the AC load, also review 4 papers?
I believe that if you do it once (i.e. not try all combinations of annotators from the two sets) and the annotators from both groups have annotated separate sections of the data then it should be fine to combine them into one
Hi @miserlis_ thanks for the great questions! Wilcoxon would work as a non-parametric test, however, when possible, it is always recommended to apply a parametric test.
For smaller datasets, we can no longer apply t-test so we propose using its non-parametric counterparts.