[HN Gopher] Rerank 3: A new foundation model for efficient enter...
       ___________________________________________________________________
        
       Rerank 3: A new foundation model for efficient enterprise search
       and retrieval
        
       Author : bguberfain
       Score  : 35 points
       Date   : 2024-04-11 17:38 UTC (5 hours ago)
        
 (HTM) web link (txt.cohere.com)
 (TXT) w3m dump (txt.cohere.com)
        
       | bigcat12345678 wrote:
       | El5 me what is rerank model? Why 4k context window size is
       | considered large?
        
         | razodactyl wrote:
         | Imagine you have 100 documents in a database and you "query"
         | the documents and return 20 candidate results.
         | 
         | Similarity gave you 20 results but Re-ranking sorted those
         | results further providing relevance.
         | 
         | That 4K is per document.
         | 
         | Edit: With sorted relevance, you can drop the lower scoring
         | documents according to the model's confidence that the
         | information in the subset is adequate to answer the query.
        
           | bigcat12345678 wrote:
           | 20 results, each have 4k snippet, feed to this ranking model,
           | ranking model produces a score based on query
           | 
           | Is this a correct understanding?
        
       | dvt wrote:
       | Being as charitable as possible here, and Rerank 3 might be the
       | bee's knees, but the examples are absolutely _awful_. Do you
       | really need to use embeddings + a large language model to search
       | for  "action" and "Christian Bale" in two columns[1]?
       | 
       | Your interface can literally just be two dropdowns. I'd like to
       | see things like "the actor that played the Joker in that movie
       | about Bob Dylan" if you're really trying to flex your semantic
       | search muscles.
       | 
       | [1]
       | https://colab.research.google.com/drive/1sKEZY_7G9icbsVxkeEI...
        
       | esafak wrote:
       | Someone correct me if I'm mistaken, but Cohere appears to be
       | using BM25 and semantic search (Embed Multilingual) individually
       | as baselines in order to look better. A more suitable baseline
       | would be the Reciprocal rank fusion (RRF) of BM25 and semantic
       | search. And those latencies seem high; seconds to rerank?
        
       ___________________________________________________________________
       (page generated 2024-04-11 23:02 UTC)