[HN Gopher] Rerank-2.5 and rerank-2.5-lite: instruction-followin...
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       Rerank-2.5 and rerank-2.5-lite: instruction-following rerankers
        
       Author : fzliu
       Score  : 43 points
       Date   : 2025-08-12 06:12 UTC (1 days ago)
        
 (HTM) web link (blog.voyageai.com)
 (TXT) w3m dump (blog.voyageai.com)
        
       | skerit wrote:
       | I read the introductory post but I still don't quite understand
        
         | daemonologist wrote:
         | A re-ranker takes a query and a chunk of text and assigns them
         | a relevance score according to how well the text answers the
         | query. (Generally - in theory you could have some other metric
         | of relevance.)
         | 
         | They're called "re"rankers specifically because they're usually
         | downstream of a faster but less accurate relevance algorithm
         | (some kind of full text search and/or vector similarity) in a
         | search pipeline. Rerankers have to run from scratch on every
         | query-document pair and are relatively computationally
         | expensive, and so are practical to run only on a small number
         | of documents.
         | 
         | An "instruction following" reranker basically just has a third
         | input which is intended to be used kind of like a system prompt
         | for an LLM - to provide additional context to all comparisons.
        
         | gnulinux wrote:
         | Rerankers are used downstream from an embedding model.
         | Embedding models are "coarse" so they give false positives for
         | things that may not be as relevant as contender text. Re-
         | ranker, ranks bunch of text based on a query in order to find
         | the most relevant ones. You can then take them and feed them as
         | context to some other query.
        
       | sroussey wrote:
       | Not really sure why they have a HuggingFace presence.
       | 
       | https://huggingface.co/voyageai/rerank-2.5-lite
        
         | xfalcox wrote:
         | Having a public tokenizer is quite useful, specially for
         | embeddings. It allows you to do the chunking locally without
         | going to the internet.
        
       | jpctan wrote:
       | Hi fzliu,
       | 
       | Have you considered to add keyword search and other factors into
       | the re-ranker?
       | 
       | Other factors are formatted texts like bold, heading, bullet
       | points, as well as bunch of factors typically seen in web search
       | techniques?
        
         | mediaman wrote:
         | Keyword search (or something similar in concept, like bm25)
         | would typically be first stage, rather than second, since it
         | can be done with an inverted index.
        
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       (page generated 2025-08-13 23:00 UTC)