<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>

CC BY-SA 4.0
2024-12-12T18:23:45.280Z
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!
Mario
CC BY-SA 4.0
2024-12-12T12:14:29.380Z
2024-12-12T12:14:29.380Z
r000bin
<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>

<large-language-models><generative-model><chatgpt><prompt>
Why GenAI tools (ChatGPT) hesitate to return the task including to return doc info like tabular data more than certain records?
Mario
# 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!  

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