Compressing Sequences in the Latent Embedding Space: $K$-Token Merging for Large Language Models

Researchers propose K-Token Merging, a compression technique that groups token embeddings in latent space to reduce computational overhead in LLM inference. The method uses a lightweight encoder to merge K consecutive tokens into single embeddings, then processes the compressed sequence through a LoRA-adapted model while preserving original vocabulary output.

MentionsK-Token Merging · LoRA

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