Semanlink - NLP and Search

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NLP and Search

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LM + Search
- ChatGPT+Search
  - Bing+ChatGPT
  - ChatGPT Retrieval Plugin
  - Knowledge (Raphaël)
  - paper-QA
  - perplexity.ai
- DSPy (Demonstrate-Search-Predict)
NLP based IR
- Neural Search
  - BEIR
  - Cherche (Raphaël)
  - ColBERT
  - Dense Passage Retrieval
    - Dual encoders (IR)
  - Embeddings in Information Retrieval
    - DSSM (Deep Semantic Similarity Model)
  - Extractive Search
    - SPIKE-KBC
  - Haystack
    - Haystack Annotation Tool
  - Hybrid Search
    - Vespa
  - Multi-Vector Retrieval
    - ColBERT
      [2211.01267] Multi-Vector Retrieval as Sparse Alignment
      
      Tags:
      
      Arxiv Doc
      Multi-Vector Retrieval
      
      2023-04-07 About
      [2304.01982] Rethinking the Role of Token Retrieval in Multi-Vector Retrieval
      
      Tags:
      
      Arxiv Doc
      ColBERT
      Multi-Vector Retrieval
      Neural Search
      NLP@Google
      
      > Multi-vector retrievers like [ColBERT](tag:colbert) are powerful, but they come at the cost of complicated inference. In this paper, we ask: "can token retrieval alone achieve great performance in multi-vector retrieval?" [tweet](https://twitter.com/leejnhk/status/1643632578824396805?s=20) > The key insight of XTR is that the token-retrieval in multi-vector models should be **trained to retrieve the most salient and informative document tokens**, so that the score between a query and document can be computed using only the retrieved information, just like how single-vector retrieval models work > This is an *amazing* way to re-engineer the scoring mechanism of late interaction / ColBERT retrievers! [src: ColBERT's author Omar Khattab](https://twitter.com/lateinteraction/status/1643439889902637056?s=20) - scoring using only retrieved document terms - imputing missing token scores using their upper bound
      
      2023-04-05 About
  - Neural Ranking Models
  - Pinecone
  - REALM
  - txtai
  - Vespa
  - Weaviate
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