
multiling nlp, evaluation, creative writing; Now: assistant prof @sfu_cs_ai 🇨🇦 Senior Researcher @Microsoft 🇺🇸 Postdoc @UMASS_NLP 🇺🇸 PhD @UTokyo_News 🇯🇵
Vancouver, British Columbia
Joined October 2011
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Marzena Karpinska retweeted
Please do not submit LLM-written papers to TACL! I appreciate many people (even high-school students) have success doing this with ARR, Neurips, and other mega-conf. But TACL reviewing is still almost all done by people, and LLM/AI slop is very unlikely to be accepted.
Marzena Karpinska retweeted
🧵 An insightful, inspiring, and slightly provoking @emnlpmeeting keynote by @gneubig where he tries to advise on:
1. If NLP research outside of industry is still valuable and viable.
2. How to choose an important problem.
3. How to make an impact.
4. And how many EMNLP papers to publish. ↩️
Marzena Karpinska retweeted
I'm happy to be presenting my poster at EMNLP in the poster slot 248!
Don't hesitate to come and discuss about it !
Yves will be presenting our work today 4:45pm in hall H! Please come and chat with him #EMNLP2026
Yves will be presenting our work today 4:45pm in hall H! Please come and chat with him #EMNLP2026
📚 AI-written stories get attention. AI-translated literature is reaching readers more quietly, shaping how they experience an author’s work.
So what is lost in AI literary translation ⁉️🤔
🤖 AI translation into English is readable and often ‘fine’.
✍️ But human translation is valued more, especially on a closer look, for clarity and immersion.
‼️ Both vary in quality BUT AI varies more, even within a single book.
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Marzena Karpinska retweeted
We're heading to Vancouver for the Futureproof Festival of AI, presented by BC + AI Ecosystem Association!
Join Canada CIFAR AI Chair Angelica Lim and Hanna Demoz from our Startups team will be in there! Make sure to come say hi! 👋
Learn More:
Marzena Karpinska retweeted
What is TMLR doing under a 300% increase in submissions?
- Suspensions of >150 authors
- Publicly naming major violations
- Submission quotas distinguishing slop patterns
- Desk rejections 6% → 53%; focus on clarity
- AI reviews evaluating soundness
blog.tmlr.org/2026/adapting-…
Great student researcher opportunity at GDM
We're planning to host a student as part of the Google DeepMind student researcher program. The goal of the project is to understand what makes LLM-human communication effective, and to use the findings to improve models.
Good project for PhD students interested in user simulation, HCI, multi-turn interaction/RL, computational cognitive science, or related topics. This is a 24-week position in NYC. Submit an expression of interest here: forms.gle/sHQnbJbHY8SM6g5NA
Marzena Karpinska retweeted
We're planning to host a student as part of the Google DeepMind student researcher program. The goal of the project is to understand what makes LLM-human communication effective, and to use the findings to improve models.
Good project for PhD students interested in user simulation, HCI, multi-turn interaction/RL, computational cognitive science, or related topics. This is a 24-week position in NYC. Submit an expression of interest here: forms.gle/sHQnbJbHY8SM6g5NA
Marzena Karpinska retweeted
Today, we launched Pangram 4.1.
We better calibrated Pangram's confidence, and we now show this tooltip indicating an AI range for documents we are less certain about.
Marzena Karpinska retweeted
📢 Attending #EMNLP2026?
Join the ARR Editors-in-Chief Meet’n’Greet (BoF) to learn how ARR works and discuss the challenges the EiC team faces.
Date: Oct 26, 2026
Time: 09:00–10:30 (Session 5)
Room: G11
#ARR #NLProc
Marzena Karpinska retweeted
An alternative interpretation: "we created a ton of tech debt and failed to optimize the right thing because we thought evals weren't important enough to prioritize building ourselves" ¯\_(ツ)_/¯
META’S HEAD OF AI SLAMS AI BENCHMARKS
"it’s some PhD student who made these benchmarks"
"you’re kind of outsourcing all of your product thinking to this viral benchmark"
"so clearly that’s wrong"
I think there is something to this. the whole industry developed so rapidly that there was hardly any time to establish proper benchmarking. companies used what was available, competitors then had to catch up on the same benchmarks, and some of them eventually became industry standards
apparently there is quite a lot of randomness and chance in how we are measuring AI progress today
Marzena Karpinska retweeted
I’m looking for a Ph.D. research intern in Human-centered NLP with a focus on Multilinguality to come work at Apple MLR (Machine Learning Research team), in the SF Bay Area, CA! ☀️
Application details in thread below 🧵👇
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Three times is the charm, still trying to find more people who would be willing to help us with 30-40min post #EMNLP interviews
Going to EMNLP 2026 in Budapest?
We are looking for 20-30 EMNLP attendees to help us understand how you handle language barriers during a conference trip.
Contact Yujun Wang: [email protected]
Please share!
@EhudReiter @mar_kar_ Pinzhen Chen, Yujun Wang
Marzena Karpinska retweeted
Congratulations to Professor Martin Ester on being named a Canada CIFAR AI Chair! 🎉
His research advances machine learning, scientific discovery, generative molecular modelling, and precision medicine.
Learn more about his research, link: cifar.ca/cifarnews/2026/10/0…
Researchers have developed a detector that aims to tell the difference between ideas made by humans and those conjured by artificial intelligence
go.nature.com/4evHZpg
Marzena Karpinska retweeted
Headed to #EMNLP2026? Want to learn about the latest advances in tokenization research in LLMs?
Come check out our tutorial on October 28. We will cover everything from fundamentals and emerging approaches with plenty of hands on demos and exercises.
See you in Budapest!
Marzena Karpinska retweeted
Andrej is not wrong about this. But if he's right that only a small caste of 5000 people, most of whom with substantial financial interests in AI companies, genuinely understand AI, what does that mean for democracy, regulation and governance?
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel.
- Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all.
- Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract.
- Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code.
- And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while.
The funnel is driven by a combination of factors:
1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel.
2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel.
3. Access. Free-tier, paid-tier, internal.
So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.

































