SpeLLM converts a standard token-based LLM into a character-spelling model with multiple parallel output heads, achieving competitive downstream performance with a 5.1% average decoding speedup.
Language model tokenizers introduce unfairness between languages
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SpeLLM: Character-Level Multi-Head Decoding
SpeLLM converts a standard token-based LLM into a character-spelling model with multiple parallel output heads, achieving competitive downstream performance with a 5.1% average decoding speedup.