ConvexTok uses convex relaxation of tokenization to a linear program, improving intrinsic metrics, bits-per-byte, and some downstream tasks while certifying near-optimality within 1% at typical vocabulary sizes.
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Pith papers citing it
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cs.CL 4years
2026 4verdicts
UNVERDICTED 4representative citing papers
Active information seeking via search tools, when combined with multi-candidate context pruning during training, produces consistent gains on translation, health, and reasoning tasks over naive tool addition or no-tool baselines.
Informativeness and diversity of samples selected by active learning show no correlation with test performance on translation tasks using few samples; ordering and pre-training effects dominate instead.
BM25 retrieval makes many-shot ICL for low-resource MT roughly 5x more data-efficient, with 50 examples matching 250 random ones and 250 matching 1000.
citing papers explorer
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Tokenisation via Convex Relaxations
ConvexTok uses convex relaxation of tokenization to a linear program, improving intrinsic metrics, bits-per-byte, and some downstream tasks while certifying near-optimality within 1% at typical vocabulary sizes.
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Context Training with Active Information Seeking
Active information seeking via search tools, when combined with multi-candidate context pruning during training, produces consistent gains on translation, health, and reasoning tasks over naive tool addition or no-tool baselines.
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Testing the Assumptions of Active Learning for Translation Tasks with Few Samples
Informativeness and diversity of samples selected by active learning show no correlation with test performance on translation tasks using few samples; ordering and pre-training effects dominate instead.
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An Empirical Study of Many-Shot In-Context Learning for Machine Translation of Low-Resource Languages
BM25 retrieval makes many-shot ICL for low-resource MT roughly 5x more data-efficient, with 50 examples matching 250 random ones and 250 matching 1000.