Body: <p>It seems that nowadays lots of people building tools to extract data from different sources (e.g. PDF reports) with the help of models like GPT-4, LLama or Falcon.</p>
  <p>I was wondering, if this really works, because from what I know, you could train GenAI models to reproduce entities in the same way as they are written in the original text, but still produce the output themselves instead of pointing to the parts of the text.</p>
  <p>So I was wondering if Extraction can be achieved reliably with GenAI or if ExtractiveAI with Bert-like models (e.g. SpanMarker for NER) is the way to go?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-09-14T11:11:12.033Z
Comments.Text: just because they can, doesn't mean that they are as good... and even if they are, running GPT-4 with respect to a Bert-like specialized model, is some order of magnitudes more expensive
Comments.UserDisplayName: Alberto
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-12-02T17:26:43.780Z
Comments.Text: i have the same question. I would like to build a q&a system but it should be reliable. Extractive q&a systems extract answers from the provided context. That means you have no hallucination. You could have a wrong extracted answer, but the answer will be always from existing content and not generated. While generated answers could be totally wrong and seem like correct.
  As far as I understand, its easier to build a q&a system with generative models like GPT4 because they have a crazy capability to also extract the answer but it could still be wrong (using RAG).
Comments.UserDisplayName: Khan
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-12-02T17:31:37.487Z
Comments.Text: on the other hand using extractive q&a you need training data to adapt the model to your specific domain. That requires more work. But as I said, I am also thinking about all this stuff since I am relatively new in this area
Comments.UserDisplayName: Khan
Tags: <named-entity-recognition><information-retrieval>
Title: Can I use Generative AI to reliably extract data (vs. Extractive AI)?
UserDisplayName: T-Dog
ThreadText: # Can I use Generative AI to reliably extract data (vs. Extractive AI)?
  
  (Asked by: username_0 on 2023-09-14)
  
  It seems that nowadays lots of people building tools to extract data from different sources (e.g. PDF reports) with the help of models like GPT-4, LLama or Falcon.
  
  I was wondering, if this really works, because from what I know, you could train GenAI models to reproduce entities in the same way as they are written in the original text, but still produce the output themselves instead of pointing to the parts of the text.
  
  So I was wondering if Extraction can be achieved reliably with GenAI or if ExtractiveAI with Bert-like models (e.g. SpanMarker for NER) is the way to go?
  
  **Tags:** named-entity-recognition, information-retrieval
  > **Comments:**
  > **username_1**: just because they can, doesn't mean that they are as good... and even if they are, running GPT-4 with respect to a Bert-like specialized model, is some order of magnitudes more expensive  
  > **username_2**: i have the same question. I would like to build a q&a system but it should be reliable. Extractive q&a systems extract answers from the provided context. That means you have no hallucination. You could have a wrong extracted answer, but the answer will be always from existing content and not generated. While generated answers could be totally wrong and seem like correct. As far as I understand, its easier to build a q&a system with generative models like GPT4 because they have a crazy capability to also extract the answer but it could still be wrong (using RAG).  
  > **username_2**: on the other hand using extractive q&a you need training data to adapt the model to your specific domain. That requires more work. But as I said, I am also thinking about all this stuff since I am relatively new in this area  
  
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  ## 0 answers
  
  