OpenBMB 提出了 Trident 方法，用以解决多模态检索中不相关文本得分高于语义对齐图像的“文本混乱”现象。该方法联合训练文本、图像和融合视图，在 6 个视觉文档检索基准上将 Trident-Qwen3VL-2B 的 Mix Avg NDCG@10 从 40.12 提升至 73.90，相关代码和论文已开源。
🔱 Why can irrelevant text outrank an image that actually contains the answer?
We find that multimodal retrievers can perform well on text-only and image-only corpora, yet degrade sharply when text and images are mixed.
We call this phenomenon "Chaos in the Text."
Our analysis reveals a systematic Modality Preference: text often receives higher similarity scores than semantically aligned images. We introduce Trident, which jointly trains Text / Image / Fused views with Multi-Positive View InfoNCE.
On 6 visual-document retrieval benchmarks, Trident-Qwen3VL-2B improves Mix Avg NDCG@10 from 40.12 → 73.90.
📄 Paper/Project: https://chaos-in-texts.github.io/
💻 Code: https://github.com/OpenBMB/Trident
#MultimodalRetrieval #MultimodalRAG #RAG #VLM #Embedding #Trident #ChaosInTheText #OpenBMB
来源：OpenBMB · x.com