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RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models

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Manage episode 380201538 series 3448051
Content provided by Arize AI. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Arize AI or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

We discuss RankVicuna, the first fully open-source LLM capable of performing high-quality listwise reranking in a zero-shot setting. While researchers have successfully applied LLMs such as ChatGPT to reranking in an information retrieval context, such work has mostly been built on proprietary models hidden behind opaque API endpoints. This approach yields experimental results that are not reproducible and non-deterministic, threatening the veracity of outcomes that build on such shaky foundations. RankVicuna provides access to a fully open-source LLM and associated code infrastructure capable of performing high-quality reranking.
Find the transcript and more here: https://arize.com/blog/rankvicuna-paper-reading/

To learn more about ML observability, join the Arize AI Slack community or get the latest on our LinkedIn and Twitter.

  continue reading

34 episodes

Artwork
iconShare
 
Manage episode 380201538 series 3448051
Content provided by Arize AI. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Arize AI or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

We discuss RankVicuna, the first fully open-source LLM capable of performing high-quality listwise reranking in a zero-shot setting. While researchers have successfully applied LLMs such as ChatGPT to reranking in an information retrieval context, such work has mostly been built on proprietary models hidden behind opaque API endpoints. This approach yields experimental results that are not reproducible and non-deterministic, threatening the veracity of outcomes that build on such shaky foundations. RankVicuna provides access to a fully open-source LLM and associated code infrastructure capable of performing high-quality reranking.
Find the transcript and more here: https://arize.com/blog/rankvicuna-paper-reading/

To learn more about ML observability, join the Arize AI Slack community or get the latest on our LinkedIn and Twitter.

  continue reading

34 episodes

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