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tokenization

topic7 events
papersTODAY 04:00 UTC

arXiv paper proposes learning tokenization end-to-end via reinforcement learning

A new arXiv preprint argues that tokenization remains a fixed, hand-designed compression step in large language model pipelines even as other components become trainable end-to-end. The authors report that reinforcement learning can be used to learn tokenization jointly with the model, with earlier work showing promise at scale. The paper appears as a cross-listed replacement submission in cs.AI and cs.LG.

tokenization/reinforcement-learning/llm/arxiv
papersTODAY 04:00 UTC

Mimir paper proposes multilingual concept modeling beyond token-based LMs

A revised arXiv paper titled Mimir argues that current language modeling is organized around tokens, where corpora are split into tokens and models are trained on token-level objectives such as next-token prediction. The authors propose an alternative that works with concepts at a large multilingual scale. The submission is a replacement version of a cross-listed paper.

multilingual/research/language-models/arxiv
papersTODAY 04:00 UTC

Modular tokenizers proposed for efficient multilingual LLMs

A new arXiv paper argues that multilingual LLMs suffer from using one shared vocabulary across all supported languages, which produces uneven compression rates between languages. The authors also note that large embedding and output matrices raise memory demands and slow processing. Their proposed modular tokenizer design assigns separate tokenization components per language to address both issues.

multilingual/tokenization/llm-efficiency/research
papersTODAY 04:00 UTC

Study compares eight tokenization strategies for ECG transformer models

A new arXiv paper examines how different tokenization choices affect ECG transformer models, since the tokenizer decides both the physiological signal content the model sees and the sequence length attention operates over. The authors benchmark eight tokenization strategies across four architectures — Transformer, Informer, Reformer, and FEDformer — on the nine-label CPSC ECG dataset. The work is cross-listed in cs.AI and cs.LG.

ecg/tokenization/transformers/benchmark
papersTODAY 04:00 UTC

Study finds punctuation and function words carry outsized context in LLM memory

A new arXiv paper introduces methods to measure how large language models store and encode contextual information. The authors report that tokens usually treated as trivial, such as determiners and punctuation marks, hold unexpectedly high contextual weight, and that removing them measurably affects model behavior. The work offers a way to probe internal context retention in transformers.

interpretability/llm/research/tokenization
papersSEP 10 04:00 UTC

TokEval: An Evaluation Suite for Language Model Tokenizers

Researchers have released TokEval, a benchmark suite designed to compare tokenizers for language models in a systematic way. The work responds to the common practice of picking tokenizers with little scrutiny, even though tokenization decisions can influence what a model is ultimately able to do. By measuring tokenizer properties alongside their downstream effects, the suite aims to give practitioners a more rigorous basis for choosing one.

tokenizers/benchmarks/evaluation/nlp
papersSEP 10 04:00 UTC

SurF: A Generative Model for Multivariate Irregular Time Series Forecasting

Researchers introduce SurF, a generative model designed for multivariate event streams that are sampled at irregular intervals. The work argues that tokenization-based approaches struggle when the gaps between events span orders of magnitude, and proposes an alternative suited to such data. The paper is a revised arXiv submission in machine learning.

time-series/generative-models/arxiv/forecasting