NLP Postdoc @ Stanford
Joined November 2019
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So honored to be on the same list as these amazing people 🤩
לקראת ראש השנה אנחנו מפרסמים את TLV AI 50: רשימה של 50 חוקרים ויזמים ישראלים בעלי ההשפעה הגדולה ביותר השנה בתחום ה-AI. חוקרים שהרעיונות שלהם מעצבים איך המודלים המודרניים חושבים, רואים ומסיקים, ויזמים שהופכים את הרעיונות האלה לשבבים, לחברות ולמוצרים שהעולם רץ עליהם.
הרשימה המלאה: 2026.tlvai50.com
30 חוקרים, 20 יזמים, 7 מאמרים. חשוב לציין שזה לא דירוג, אלא תמונת מצב של הישראלים המשפיעים ביותר בעולם מחקר ה-AI.
כמה מגמות שבולטים:
- ניאו-לאבס ישראליים סוף סוף צצים בשכבת המודלים
- אבטחת AI: דור חדש של AI natives מגדיר איך מחסנים ומאמתים מערכות - וישראל בחזית
- ראייה ממוחשבת: כמה מהמעבדות המצוטטות בעולם, עם סטארטאפים שהופכים את הכישרון הזה ל-physical AI
ולא רק: 7 מאמרים ראויים לציון של חוקרים ישראלים צעירים, מתחת לרשימה.
#ICLR2026 @iclr_conf 🇧🇷
Presenting "From Concepts to Thoughts": using information theory to compare how LLMs and humans represent concepts.
tl;dr: encoder LLMs align more with humans than decoders; humans are less efficient. Maybe that “inefficiency” is a feature, not a bug?
Chen Shani retweeted
I forgot to update that the paper got accepted to ICLR. Fun fact, I did my PhD in computational neuroscience, but I hated cognitive science classes. So lucky that @ChenShani2 actually is an expert in it!
You know all those arguments that LLMs think like humans? Turns out it's not true.
🧠 In our paper "From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning" we test it by checking if LLMs form concepts the same way humans do @ylecun @ChenShani2 @jurafsky
Chen Shani retweeted
How can we help LLMs move beyond the obvious toward generating more creative and diverse ideas?
In our new TACL paper, we propose a novel approach to enhance LLM creative generation!
arxiv.org/abs/2504.20643
@ChenShani2 @GabiStanovsky @jurafsky @HyadataLab @stanfordnlp @nlphuji
Chen Shani retweeted
New research shows LLMs favor compression over nuance — a key reason they lack human-like understanding.
By Stanford postdoc @ChenShani2, CDS Research Scientist @ziv_ravid, CDS Founding Director @ylecun, & Stanford professor @jurafsky.
nyudatascience.medium.com/th…
Chen Shani retweeted
You know all those arguments that LLMs think like humans? Turns out it's not true.
🧠 In our paper "From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning" we test it by checking if LLMs form concepts the same way humans do @ylecun @ChenShani2 @jurafsky
Chen Shani retweeted
@ylecun and I have been pondering the concept of optimal representation in self-supervised learning, and we're excited to share our findings in a recently published paper! 📝🔍 arxiv.org/abs/2304.09355
At NAACL??
I'm presenting "Rethinking Word Similarity: Semantic Similarity through Classification Confusion" at 4:15 pm today (Ruidoso room)
@KaitlynZhou @danedels @jurafsky
Human-inspired measure of semantic similarity with cool real-world applications!
aclanthology.org/2025.naacl-…
Chen Shani retweeted
🚨🚨New Working Paper🚨🚨
AI-generated content is getting more politically persuasive. But does labeling it as AI-generated change its impact?🤔
Our research says the disclosure of AI authorship has little to no effect on the persuasiveness of AI-generated content.
🧵1/6
Chen Shani retweeted
#ELSCspecialseminar with Dr. Chen Shani on the topic of “Designing Language Models to Think Like Humans” will take place on Tuesday, June 25, at 14:00 IST. Come hear the lecture at ELSC: Room 2004, Goodman bldg.
@ChenShani2
Stanford NLP Retreat!
It was a packed weekend, full of
great people and activities
(And the car broke down halfway back, another great adventure!)
Stanford NLP Retreat 2024!
@RyanCLouie and I organized a PowerPoint Karaoke 🎤
My favorite part is Chris' answer:
Q: What is the first principle component for both babies and undergrads?
Chris Manning: HUNGER!
@ChrisManning @stanfordnlp
One of the T-shirts I designed and printed as a farewell gift for my PhD supervisor (this is my favorite one)
Chen Shani retweeted
🚀 Excited to share our latest paper about the sensitivity of LLMs to prompts!
arxiv.org/abs/2401.00595
Our work may partly explain why some models seem less accurate than their formal evaluation may suggest. 🧐
@guymkaplan, @malk_dan, @DrorRotem, @HyadataLab, @GabiStanovsky
Chen Shani retweeted
The language people use when they interact with each other changes over the course of the conversation.
🔍 Will we see a systematic language change along the interaction of human users with a text-to-image model?
#EMNLP23
arxiv.org/abs/2311.12131
W @LChoshen @AbendOmri
🧵👇
Chen Shani retweeted
Excited to share our new @biorxivpreprint presenting DISCOVER, a generalized method toward systematic visual interpretability of image-based classification models!
Project led by @oded_rotem in collaboration with @AiVFtech!
biorxiv.org/cgi/content/shor…
🧵
1/n
Chen Shani retweeted
[1/9] 🚨 We present our recent @GoogleAI project: The Chosen One --- a fully automated solution for the task of consistent character generation in text-to-image diffusion models 🧑🎨.
Project Page: omriavrahami.com/the-chosen-…
Chen Shani retweeted
🎉Excited to announce our paper's acceptance at #EMNLP2023! We explore a fascinating question: When faced with (un)answerable queries, do LLMs actually grasp the concept of (un)answerability?🧐 This work is a collaborative effort with @clu_avi @ravfogel @omerNLP and Ido Dagan 1/n
1. I'm officially a Ph.D! Thank you for everything @HyadataLab!
2. I'll present 2 papers at EMNLP:
Towards Concept-Aware LLMs (lnkd.in/dji73KXj)
FAME: Flexible, Scalable Analogy Mappings Engine (lnkd.in/dxX8YsUP)
@drjilles @HyadataLab
@HebrewU @HebrewU_heb