LLMs exhibit the Position Curse, with backward position retrieval in lists lagging far behind forward retrieval, showing only partial gains from PosBench fine-tuning.
The strawberry problem: Emergence of character-level understanding in tokenized language models
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Subword tokenization's main benefits arise from higher sample throughput and the use of subword boundaries as explicit priors or inductive biases, isolated via controlled byte-level simulations.
citing papers explorer
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The Position Curse: LLMs Struggle to Locate the Last Few Items in a List
LLMs exhibit the Position Curse, with backward position retrieval in lists lagging far behind forward retrieval, showing only partial gains from PosBench fine-tuning.
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Decoupling the Benefits of Subword Tokenization for Language Model Training via Byte-level Simulation
Subword tokenization's main benefits arise from higher sample throughput and the use of subword boundaries as explicit priors or inductive biases, isolated via controlled byte-level simulations.