From Tokens to Words: On the Inner Lexicon of LLMs
Keywords: Detokenization, Large Language Models, LLM, Byte-Pair Encoding, BPE, Subword Tokens, Word Reconstruction, Latent Lexicon, Inner Dictionary, Token Aggregation, Feed-Forward Networks, FFNs, Out-of-Vocabulary Words, Efficiency, Tokenization, Language Model Optimization
TL;DR: We provide evidence that LLMs use an inner lexicon to reconstruct words from sub-word tokens. We thoroughly analyze this detokenization process to understand how LLMs manage words internally, and demonstrate the potential gains in efficiency.
Abstract: Natural language is composed of words, but modern large language models (LLMs) process sub-words as input. A natural question raised by this discrepancy is whether LLMs encode words internally, and if so how. We present evidence that LLMs engage in an intrinsic detokenization process, where subword sequences are combined into coherent whole-word representations at their last token. Our experiments show that this process primarily takes place within the early and middle layers of the model. We further demonstrate its robustness to arbitrary splits (e.g., “cats” to “ca” and “ts”), typos, and importantly—to out-of-vocabulary words: when feeding the last token internal representations of such words to the model as input, it can “understand” them as the complete word despite never seeing such representations as input during training. Our findings suggest that LLMs maintain a latent vocabulary beyond the tokenizer’s scope. These insights provide a practical, finetuning-free application for expanding the vocabulary of pre-trained models. By enabling the addition of new vocabulary words, we reduce input length and inference iterations, which reduces both space and model latency, with little to no loss in model accuracy.
Primary Area: interpretability and explainable AI
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Submission Number: 6410
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