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!

[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

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!!🌟🌟

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

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* ✅

🔥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.

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

In our new preprint, @roireichart ,@DrorRotem and I propose a new statistical procedure: The Alternative Annotator Test (alt-test) The goal? To help researchers justify using LLMs over humans—if the LLM passes, its annotations can be confidently trusted😎 arxiv.org/abs/2501.10970

Check out our new pre-print on statistically sound methodology to verify the quality of LLM-as-a-judge annotation. @NitCal @DrorRotem

A few months ago I told you that I'm working on something awesome and I need some datasets...well here is the first output of that effort and I sincerely think it's amazing 🤩 check it out! @NitCal @roireichart #llm-as-a-judge, #nlpevaluation

Do you think LLMs could win a Nobel Prize one day? 🤔 Can NLP predict heroin addiction outcomes, uncover suicide risks, or simulate brain activity? 🧠 @lotemi_peled, @roireichart, and I wrote a blog post about NLP for Human-Centric Sciences 🤖👩‍🔬 👇👇👇 nitaytech.github.io/blog/202…