long-tail-knowledge

Seed2.0 is a new model series that addresses complex real-world tasks by improving long-tail knowledge, instruction following, reasoning, visual understanding, and search capabilities. It presents a robust evaluation framework grounded in user needs.

A Harvard research paper introduces Recoding-Decoding (RD), a novel decoding scheme that injects random priming phrases and diverting tokens to tap into an LLM's long-tail knowledge, significantly boosting output diversity without fine-tuning. The method maintains high relevance while mitigating response homogenization, with stronger models showing greater diversity gains.