做文档理解或长文本AI应用的团队值得关注——字节跳动用提问替代转录，让7B模型在长文档任务上超越大模型，直接降低了计算成本，建议点开看看具体方法。
字节跳动Seed团队研究发现，通过提问方式训练7B参数的多模态大模型（LMM），在处理长文档（尤其是图像密集型文档）时，其可靠性甚至超过更大规模的模型。该模型能处理比训练时见过的文档长四倍的内容，且无需逐页转录文本，而是通过自主寻找相关段落来回答问题。这一方法显著提升了长文档问答的效率与准确性，为多模态模型在复杂文档理解任务中的应用提供了新思路。
ByteDance study finds that asking LMMs questions beats making it transcribe text for long document training
ByteDance Seed shows that a 7B model can answer questions on long, image-heavy documents more reliably than much larger models, even when documents are four times longer than anything it saw during training. Instead of transcribing pages, the model learns by answering questions and finding the right passages on its own. The article ByteDance study finds that asking LMMs questions beats making it transcribe text for long document training appeared first on The Decoder .