The project involves technology:
- mysql
- python
- origin
- spss
file | describe |
---|---|
q&a.sql | Question & answer pairs raw data, including title(question sample), chatGPT response (ai_reply), human answer (reply), perplexity value of human answer and perplexity value of chatGPT response (ai_ppl) |
q&a_corpus.sql | Human answer and chatGPT response Part-of-Speech classification table |
First, run the database.sql
file to create a data table, and then import data from q&a.sql
and q&a_corpus.sql
into the data table.
run this sql, You will receive the raw data of Figure 2.
SELECT
COUNT(1) / 5219 num, type, corpus
FROM
chatgpt.chatgpt_qa_corpus
GROUP BY type , corpus
ORDER BY num DESC , corpus;
After processing the data in the origin, you will get Figure 2. Of course, you can also directly run origin project/paper3_ figure2.opj to get figure2.png
You can compare human in Excel or other tools to conduct statistics on human_ppl and ai_ppl, or you can directly run origin project/paper3_ figure 3.opj obtains figure 3.png