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Entity tracking emerges in sub-billion parameter language models and exceeds human performance in naturalistic narratives

arXiv自然语言 2026-08-20 12:00 1 阅读 查看原文

Computer Science > Computation and Language

arXiv:2608.18083 (cs)

Title:Entity tracking emerges in sub-billion parameter language models and exceeds human performance in naturalistic narratives

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Abstract:Understanding language requires tracking entities across discourse - i.e., knowing where things are and how they change, even when not explicitly stated. Whether language models perform such tracking in a human-like fashion remains unclear, in part because existing evaluations rely on artificial tasks, far removed from natural language comprehension, and lack comparisons to humans. Here, we evaluate entity tracking in both language models and humans (N = 48) using naturalistic narratives at multiple levels of complexity. In humans, we find that entity tracking degrades specifically with narrative complexity, not narrative length. In language models, we find that human-level entity tracking is already present at 410 million parameters - well below the multi-billion parameter, code-specialised models identified by prior work - and improves with scale, with contemporary models far exceeding human performance. Together, these results demonstrate that entity tracking, a core component of language understanding, emerges at model scales far smaller than previously thought.
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2608.18083 [cs.CL]
  (or arXiv:2608.18083v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2608.18083

Submission history

From: Karolina Drożdż [view email]
[v1] Thu, 4 Jun 2026 11:17:37 UTC (589 KB)
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