Tal Linzen

Updated preprint with @tallinzen.bsky.social and @shravanvasishth.bsky.social : "LLM surprisal is necessary but not sufficient to capture English garden-path effects: Evidence from joint latent modeling of reading paradigms" arxiv.org/abs/2602.04489

Deconstructing sentence disambiguation by joint latent modeling of reading paradigms: LLM surprisal is not enough arxiv.org

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New paper! LLM memory keeps improving, but this makes them *worse* as user sims. If we want to build models that can, e.g., simulate realistic students to train chatbots to be better teachers, then these models need to be able to forget like humans do 📄: arxiv.org/abs/2605.25680

[2605.25680] Simulating Human Memory with Language Models arxiv.org View on Bluesky →

To make AI better at imitating humans, limit its memory. Ex-CDS Faculty Fellow Nicholas Tomlin, CDS PhD @michahu.bsky.social, & CDS Prof @tallinzen.bsky.social show that capping a model's memory at 4 slots makes it a far better stand-in for a real person. nyudatascience.medium…

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