Answers.Body: <p>For closed-source models we can only guess why it doesn't return all the rows. For open-source models I know that there are repetition and length penalties you can adjust (<a href="https://docs.vllm.ai/en/v0.4.0.post1/dev/sampling_params.html" rel="nofollow noreferrer">https://docs.vllm.ai/en/v0.4.0.post1/dev/sampling_params.html</a>). For GPT-4o there could as well be a repetition or length penalty we don't know.</p>
  <p>Why don't you split the table in smaller chunks and send each separately to the model?</p>
  
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Answers.CreationDate: 2024-12-12T18:23:45.280Z
Answers.Text: Finally, I used this trick. I did it for 1st half from the top, then I saved the table, then queried for 2nd half, and then saved it and then merged the results!
Answers.UserDisplayName: Mario
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Answers.CreationDate: 2024-12-12T12:14:29.380Z
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Body: <p>Yesterday I gave a task to ChatGPT and noticed despite the clear step-by-step task to return the information from a simple table with 38 records, it stops to generate right response and after some back and fourth it returns me 15/16 records and I need to ask for to return me complete it for all records of table (table has multi columns):</p>
  <blockquote>
  <p>please complete the task for all records of table. you return 15 out of 38 records.</p>
  </blockquote>
  <p>an then it returns with increment of 1 records more than previous generated answer which was frustrating to chat like this and get still incomplete results.</p>
  <p>At the end I did trick and I asked to start from end of table and merge the results!</p>
  <ul>
  <li>I'm curious why it the case is for doing the clear task incompletely? I was assuming maybe there is restriction to generate long answers but returning info of a table with 38 records should not be called long task in sense of length of generated return response?</li>
  <li>Are there some prompt engineering techniques I should now except for define the task step-by-step and use right vocab?</li>
  </ul>
  <hr />
  <p><strong>Note:</strong></p>
  <ul>
  <li>I had a <strong>premium account</strong> while did this experiment and conversation.</li>
  <li>used model: <strong>GPT-4o</strong></li>
  </ul>
  
Tags: <large-language-models><generative-model><chatgpt><prompt>
Title: Why GenAI tools (ChatGPT) hesitate to return the task including to return doc info like tabular data more than certain records?
UserDisplayName: Mario
ThreadText: # Why GenAI tools (ChatGPT) hesitate to return the task including to return doc info like tabular data more than certain records?
  
  (Asked by: username_0 on 2024-11-22)
  
  Yesterday I gave a task to ChatGPT and noticed despite the clear step-by-step task to return the information from a simple table with 38 records, it stops to generate right response and after some back and fourth it returns me 15/16 records and I need to ask for to return me complete it for all records of table (table has multi columns):
  
  > please complete the task for all records of table. you return 15 out of 38 records.
  
  an then it returns with increment of 1 records more than previous generated answer which was frustrating to chat like this and get still incomplete results.
  
  At the end I did trick and I asked to start from end of table and merge the results!
  
  * I'm curious why it the case is for doing the clear task incompletely? I was assuming maybe there is restriction to generate long answers but returning info of a table with 38 records should not be called long task in sense of length of generated return response?
  * Are there some prompt engineering techniques I should now except for define the task step-by-step and use right vocab?
  
  ---
  
  **Note:**
  
  * I had a **premium account** while did this experiment and conversation.
  * used model: **GPT-4o**
  
  **Tags:** large-language-models, generative-model, chatgpt, prompt
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2024-12-12. Score: 0
  
  For closed-source models we can only guess why it doesn't return all the rows. For open-source models I know that there are repetition and length penalties you can adjust (<https://docs.vllm.ai/en/v0.4.0.post1/dev/sampling_params.html>). For GPT-4o there could as well be a repetition or length penalty we don't know.
  
  Why don't you split the table in smaller chunks and send each separately to the model?
  
  > **Comments:**
  > **username_0**: Finally, I used this trick. I did it for 1st half from the top, then I saved the table, then queried for 2nd half, and then saved it and then merged the results!  
  
  ---
  
  