Significance-Gain Pair Encoding for LLMs: A Statistical Alternative to Frequency-Based Subword Merging

Subword tokenization is a key design choice for modern language models, including large language models (LLMs), with byte/character-level BPE serving as a widely used baseline. Standard BPE selects merges by raw pair frequency, which favors compression but can conflate true adjacency cohesion with pairs that are frequent due to high marginal counts. We introduce Significance-Gain BPE, a drop-in alternative merge criterion that measures cohesion via a z-statistic under an independence null model and combines it with an explicit compression-aware gain term. We evaluate Significance-Gain BPE on WikiText-103 (raw) character slices using a small causal Transformer language model and report both token-dependent perplexity and the tokenizer-invariant metric bits-per-character (BPC). At a representative operating point, Significance-Gain BPE reduces validation and test perplexity by 13% and 12%, respectively, and improves validation and test BPC by ∼0.9–1.0%. A vocabulary-size sweep further shows lower BPC in most closest-compression comparisons, suggesting that statistically grounded merge selection can improve predictive efficiency per unit of raw text across a range of compression regimes.