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Fine-Tuning LLaMA for Multi-Stage Text Retrieval
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This story was originally published on HackerNoon at: https://hackernoon.com/fine-tuning-llama-for-multi-stage-text-retrieval.
Discover how fine-tuning LLaMA models enhances text retrieval efficiency and accuracy
Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #llama, #llm-fine-tuning, #fine-tuning-llama, #multi-stage-text-retrieval, #rankllama, #bi-encoder-architecture, #transformer-architecture, #hackernoon-top-story, and more.
This story was written by: @textmodels. Learn more about this writer by checking @textmodels's about page, and for more stories, please visit hackernoon.com.
This study explores enhancing text retrieval using state-of-the-art LLaMA models. Fine-tuned as RepLLaMA and RankLLaMA, these models achieve superior effectiveness for both passage and document retrieval, leveraging their ability to handle longer contexts and exhibiting strong zero-shot performance.
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