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Entity tracking emerges in sub-billion parameter language models and exceeds human performance in naturalistic narratives
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language modelsentity trackingnatural language processing
2608.18083
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1h ago80%
Abstract
This paper evaluates entity tracking in language models and humans, revealing that language models can achieve human-level tracking at significantly lower parameter counts than previously thought.
Reality Card
Core Claim
Entity tracking, a crucial aspect of language understanding, emerges in language models with as few as 410 million parameters and surpasses human performance in naturalistic narratives.
Method / Result
Human-level entity tracking is present at 410 million parameters.
Limitations
The study relies on naturalistic narratives, which may not fully capture all aspects of entity tracking in varied contexts.
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