Yet another example of how LLMs are not robust (a key argument against symbolic AI): a small fraction of 'bad' data can compromise models regardless of dataset or model size. Data poisoning does not scale with model size. arxiv.org/pdf/2510.07192

There is mounting evidence that LLMs rely heavily on brute-force and shallow heuristics to achieve high accuracy, despite some studies (though not all) finding evidence for sophisticated latent representations. Add another to the bunch: www.arxiv.org/abs/2506.16678

Wow, yes. An amazing glimpse of the LLM Lie Machine at work. Anyone whose students are bring tempted by LLMs, sharing this could be very helpful showing how it lies, and how it fails.

Bresnan (1991) journals.linguisticsociety.org/proceedings/...

Register now for the LSA Linguistic Institute in Eugene, OR: over 80 courses over two 2.5-week terms (July 7-22::July 24-August 8). I'm teaching Constructions & the Grammar of Context with E Francis in session 1! center.uoregon.edu/LSA/2025/

This the most recent job posting data in the Linguist List. Data and code: github.com/RuiPChaves/L...

Scientists research man missing 90% of his brain who leads a normal life www.cbc.ca/radio/asitha...

Cool paper showing that because LLMs use a trailing white space in their tokens, they incorrectly estimate probabilities (improper distribution error), and that this causes significantly different estimates of garden-path effects: arxiv.org/abs/2406.10851

I can't not miss this one. 😆

I need a German word for when a 7yo opts to play Conway's Game of Life (in Golly) rather than the usual Nintendo games. He loves the glider gun...! And experimenting with all the chaos. But don't we all?