Papers by Abishek Stephen

2 papers
TokCollate: A Comprehensive Tool for Tokenizer Evaluation and Visualization across Languages (2026.acl-demo)

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Challenge: Tokenization quality varies significantly across languages, leading to disparities in LLM performance and cost for speakers of less-resourced languages.
Approach: They propose a Python-based evaluation framework and a JavaScript visualization interface that evaluates tokenizers in a variety of languages.
Outcome: TokCollate is an evaluation framework for tokenizers with a JavaScript visualization interface.
Evaluating Morphological Plausibility of Subword Tokenization via Statistical Alignment with Morpho-Syntactic Features (2026.findings-eacl)

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Challenge: Existing metric for subword tokenization evaluation for morphological plausibility requires unavailable or inconsistent gold segmentation data.
Approach: They propose a morpho-syntactic feature-based metric for subword tokenization evaluation.
Outcome: The proposed metric correlates well with traditional morpheme boundary recall while being more broadly applicable across languages with different morphological systems.

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