Date Fragments: A Hidden Bottleneck of Tokenization for Temporal
  Reasoning
Modern BPE tokenizers often split calendar dates into meaningless fragments,
e.g., 20250312 rightarrow 202, 503, 12, inflating token counts and obscuring
the inherent structure needed for robust temporal reasoning. In this work, we
(1) introduce a simple yet interpretable metric, termed date fragmentation
ratio, that measures how faithfully a tokenizer preserves multi-digit date
components; (2) release DateAugBench, a suite of 6500 examples spanning three
temporal reasoning tasks: context-based date resolution, format-invariance
puzzles, and date arithmetic across historical, contemporary, and future
regimes; and (3) through layer-wise probing and causal attention-hop analyses,
uncover an emergent date-abstraction mechanism whereby large language models
stitch together the fragments of month, day, and year components for temporal
reasoning. Our experiments show that excessive fragmentation correlates with
accuracy drops of up to 10 points on uncommon dates like historical and
futuristic dates. Further, we find that the larger the model, the faster the
emergent date abstraction that heals date fragments is accomplished. Lastly, we
observe a reasoning path that LLMs follow to assemble date fragments, typically
differing from human interpretation (year rightarrow month rightarrow
day).
68302befc518550cc0c2e505
Gagan Bhatia
claimed_verified
2025-05-26T08:14:47.929Z
60394599033b61166496163b
https://cdn-avatars.huggingface.co/v1/production/uploads/1614366097007-noauth.jpeg
Gagan Bhatia
user
gagan3012
68302befc518550cc0c2e506
Maxime Peyrard
68302befc518550cc0c2e507
Wei Zhao
Date Fragments: A Hidden Bottleneck of Tokenization for Temporal
  Reasoning
Modern BPE tokenizers often split calendar dates into meaningless fragments,
e.g., 20250312 rightarrow 202, 503, 12, inflating token counts and obscuring
the inherent structure needed for robust temporal reasoning. In this work, we
(1) introduce a simple yet interpretable metric, termed date fragmentation
ratio, that measures how faithfully a tokenizer preserves multi-digit date
components; (2) release DateAugBench, a suite of 6500 examples spanning three
temporal reasoning tasks: context-based date resolution, format-invariance
puzzles, and date arithmetic across historical, contemporary, and future
regimes; and (3) through layer-wise probing and causal attention-hop analyses,
uncover an emergent date-abstraction mechanism whereby large language models
stitch together the fragments of month, day, and year components for temporal
reasoning. Our experiments show that excessive fragmentation correlates with
accuracy drops of up to 10 points on uncommon dates like historical and
futuristic dates. Further, we find that the larger the model, the faster the
emergent date abstraction that heals date fragments is accomplished. Lastly, we
observe a reasoning path that LLMs follow to assemble date fragments, typically
differing from human interpretation (year rightarrow month rightarrow
day).
New DateAugBench benchmarks reveal how modern tokenizers fragment dates, impacting the accuracy of temporal reasoning in large language models, which compensate for fragmentation more effectively as they grow larger.
BPE tokenizers date fragmentation ratio DateAugBench temporal reasoning tasks context-based date resolution format-invariance puzzles date arithmetic layer-wise probing causal attention-hop analyses date-abstraction mechanism large language models historical dates futuristic dates