Craig Schmidt

@craigschmidt.com

Interested in ML, AI, and NLP. Particularly interested in tokenization. Live in the Boston area and work in R&D at Kensho Technologies.

Peer review was one of the most-discussed topics at #ACL2026 . Many folks were concerned about the incredible growth in the number of ARR submissions (17K for the May26 ARR cycle 😱), and even more shocked that ~40% didn't have any authors qualified to review. What is going on?? I did some digging...

arxiv.org/abs/2605.22705 arxiv.org/abs/2605.22821 Happy Linear Programming for Tokenization day! I was involved with two separate papers that hit ArXiv yesterday, using LP's to find the vocabulary maximizing compression, depending on the kind of inference you want to use.

Tokenization with Split Trees

We introduce Tokenization with Split Trees (ToaST), a subword tokenization method that directly optimizes compression under a new recursive inference procedure. ToaST greedily splits each pretoken int...

arxiv.org

I've been using GPT-5 on my phone (since it isn't my web account yet). I've had several bad responses with logical inconsistencies. My hot take: what if GPT-5 is mostly about saving OpenAI money on inference, which is why they are deprecating all the other models so quickly.

@crampell.bsky.social’s post got me to thinking and…yes…Trump has apparently canceled the research grant of Judea Pearl, who is one of the world’s leading scholars, is Jewish, Israeli-American, & is vocally opposed to antisemitism, & is the father of Daniel Pearl. www.science.org/content/arti...

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Catherine Rampell@crampell.bsky.social · last yr.

Trump canceled the research grant for America’s — perhaps the world’s? — top mathematician (via @weakinstrument.bsky.social)

My son said he couldn’t call me on Father’s Day because he had worked the weekend dealing with a North Korean hacking group. Valid excuse I guess. The hack analysis …

alden@re.wtf · last yr.

excited bc today @huntress.com is releasing our analysis of a gnarly intrusion into a web3 company by the DPRK's BlueNoroff!! 🤠 we've observed 8 new pieces of macOS malware from implants to infostealers! and they're actually good (for once)! www.huntress.com/blog/inside-...

A bit of a mess around the conflict of COLM with the ARR (and to lesser degree ICML) reviews release. We feel this is creating a lot of pressure and uncertainty. So, we are pushing our deadlines: Abstracts due March 22 AoE (+48hr) Full papers due March 28 AoE (+24hr) Plz RT 🙏

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I wanted to post of a few of my favorite #EMNLP2024 papers, starting with a couple in tokenization. Fishing For Magicarp explores the problem of undertrained "glitch" tokens, and how they can be identified from their embedding vectors. aclanthology.org/2024.emnlp-m...

Fishing for Magikarp: Automatically Detecting Under-trained Tokens in Large Language Models

Sander Land, Max Bartolo. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

aclanthology.org

There's a known bug in how we compute "word" probabilities with subword-based LMs that mark beginnings of words -- as pointed out by Byung-doh Oh and Will Schuler, & @tpimentel.bsky.social and Clara Meister I'm pleased to announce that minicons now includes a fix which runs batch-wise!

Code: from minicons import scorer

lm = scorer.IncrementalLMScorer("gpt2-xl", "cuda:0")

stimuli = ["I was a matron in France", "I was a mat in France"]

# old way, no correction
# P.S. gpt2 does not automatically add a bos token at the beginning...
lm.token_score(stimuli, bos_token=True, surprisal=True, base_two=True, bow_correction=False)

'''Rounded Output
[[('<|endoftext|>', 0.0),
  ('I', 5.85),
  ('was', 4.28),
  ('a', 4.67),
  ('mat', 16.34),
  ('ron', 1.74),
  ('in', 2.12),
  ('France', 11.43)],
 [('<|endoftext|>', 0.0),
  ('I', 5.85),
  ('was', 4.28),
  ('a', 4.67),
  ('mat', 16.34),
  ('in', 10.78),
  ('France', 10.71)]]
'''

# the new way! notice the surprisal of "mat" in both cases
lm.token_score(stimuli, bos_token=True, surprisal=True, base_two=True, bow_correction=True)

'''Rounded Output
[[('<|endoftext|>', 0.0),
  ('I', 6.30),
  ('was', 3.84),
  ('a', 4.68),
  ('mat', 16.34),
  ('ron', 2.11),
  ('in', 1.75),
  ('France', 11.42)],
 [('<|endoftext|>', 0.0),
  ('I', 6.30),
  ('was', 3.84),
  ('a', 4.68),
  ('mat', 21.34),
  ('in', 5.80),
  ('France', 10.69)]]
'''Screenshot from Oh and Schuler showing surprisal values for the partial sentences "I was a matron in" and "I was a mat in" using GPT-2 XL with leading whitespaces and trailing whitespaces.