AdaptTok-Indic: Script-Aware Adaptive Tokenization for Indic Languages
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
The Flow has not summarised this story yet — read it at arXiv Computation and Language.
arXiv:2607. 23319v1 Announce Type: cross Abstract: Standard subword tokenization algorithms such as Byte-Pair Encoding (BPE) and SentencePiece are trained predominantly on modern language corpora and produce inefficient segmentations when applied to classical Indian languages.
Large language models (LLMs) process text through subword tokenizers rather than directly reading characters or words. Because these tokenizers are trained predominantly on English-centric corpora, they introduce a systematic and often overlooked disadvantage for many non-English languages.
arXiv:2607. 24276v1 Announce Type: cross Abstract: Large language models (LLMs) process text through subword tokenizers rather than directly reading characters or words.
IndicDetect is a benchmark for evaluating AI‑generated text detection in Hindi, Telugu, and Tamil. It pairs curated human‑written texts with LLM‑generated counterparts across multiple domains and generators, testing detectors under domain shift, generator shift, and adversarial perturbation. The study shows that supervised neural detectors fail to generalize to unseen generators and attacks, with Hindi experiencing the greatest degradation, indicating that robustness—not peak accuracy—is the main weakness in Indic language detectors.
IndicTriMix presents a new benchmark and models for token‑level language identification in tri‑language code‑mixed text involving Hindi, Gujarati, and Bengali. The authors reformulate the task as sequence labeling and fine‑tune transformer models MuRIL and XLM‑RoBERTa, evaluating them on manually annotated test sets. They also introduce two code‑mixing generation methods using parallel sentences and release the datasets and fine‑tuned models for public use.
arXiv:2506. 15138v2 Announce Type: replace-cross Abstract: Tokenization directly affects the inference efficiency of large language models, since fragmented tokenization increases sequence length and generation cost.