Modern IR Bench
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Code-native benchmark for modern IR solutions
Cheney Zhang
zc277584121
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commented on How We Built a Semantic Highlight Model To Save Token Cost for RAG 8 months ago
Yes, you can use it this way to optimize the user experience
reacted to their post with 🚀🔥 8 months ago
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We've open-sourced a bilingual Semantic Highlighting model that can power multiple production scenarios:
1) RAG Answer Highlighting — Automatically highlight the exact sentences that answer user queries, improving interpretability and helping users quickly locate relevant information.
2) RAG Noise Filtering — Prune irrelevant context before sending to LLMs, achieving 70-80% token cost reduction while improving answer quality by letting the model focus on what matters.
3) Search System Highlighting — Add semantic highlighting features to recommendation systems, e-commerce search, or any retrieval system where users need to see why a result is relevant.
Try it out: zilliz/semantic-highlight-bilingual-v1
Read our article: https://huggingface.co/blog/zilliz/zilliz-semantic-highlight-model
1) RAG Answer Highlighting — Automatically highlight the exact sentences that answer user queries, improving interpretability and helping users quickly locate relevant information.
2) RAG Noise Filtering — Prune irrelevant context before sending to LLMs, achieving 70-80% token cost reduction while improving answer quality by letting the model focus on what matters.
3) Search System Highlighting — Add semantic highlighting features to recommendation systems, e-commerce search, or any retrieval system where users need to see why a result is relevant.
Try it out: zilliz/semantic-highlight-bilingual-v1
Read our article: https://huggingface.co/blog/zilliz/zilliz-semantic-highlight-model
posted an update 8 months ago
Post
3192
We've open-sourced a bilingual Semantic Highlighting model that can power multiple production scenarios:
1) RAG Answer Highlighting — Automatically highlight the exact sentences that answer user queries, improving interpretability and helping users quickly locate relevant information.
2) RAG Noise Filtering — Prune irrelevant context before sending to LLMs, achieving 70-80% token cost reduction while improving answer quality by letting the model focus on what matters.
3) Search System Highlighting — Add semantic highlighting features to recommendation systems, e-commerce search, or any retrieval system where users need to see why a result is relevant.
Try it out: zilliz/semantic-highlight-bilingual-v1
Read our article: https://huggingface.co/blog/zilliz/zilliz-semantic-highlight-model
1) RAG Answer Highlighting — Automatically highlight the exact sentences that answer user queries, improving interpretability and helping users quickly locate relevant information.
2) RAG Noise Filtering — Prune irrelevant context before sending to LLMs, achieving 70-80% token cost reduction while improving answer quality by letting the model focus on what matters.
3) Search System Highlighting — Add semantic highlighting features to recommendation systems, e-commerce search, or any retrieval system where users need to see why a result is relevant.
Try it out: zilliz/semantic-highlight-bilingual-v1
Read our article: https://huggingface.co/blog/zilliz/zilliz-semantic-highlight-model
updated a
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upvoted an article 8 months ago
Article
How We Built a Semantic Highlight Model To Save Token Cost for RAG
zilliz
• • 67 published an article 8 months ago
Article
How We Built a Semantic Highlight Model To Save Token Cost for RAG
zilliz
• • 67 updated 5
datasets 8 months ago
zilliz/dureader-context-relevance-with-think
Viewer • Updated • 76.7k • 20
zilliz/mmarco_chinese_200k-context-relevance-with-think
Viewer • Updated • 216k • 20
zilliz/wikipedia_zh_cn_500k-context-relevance-with-think
Viewer • Updated • 500k • 20
zilliz/gooaq-context-relevance-130k-context-relevance-with-think
Viewer • Updated • 129k • 23
zilliz/natural_questions-context-relevance-with-think
Viewer • Updated • 13.4k • 21