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View Code? Open in Web Editor NEWCan ChatGPT really understand the opinions, sentiments, and emotions contained in the text? We provide a preliminary evaluation.
Can ChatGPT really understand the opinions, sentiments, and emotions contained in the text? We provide a preliminary evaluation.
Hi there!
Your work is excellent!
To manually check and understand the prediction results of different models, I am eager to find the documents storing relevant information.
I went through your work and read the code on GitHub. I understand the row data (true labels) stored in the standard data file. However, there are many files named by "50_test" "100_test" "train" "test" "dev" under different folders that I did not understand their connections with each other. To my understanding, "50_test" and "100_test" are generated by extracting 50 lines and 100 lines from "dev" respectively.
I am trying to understand these files with the purpose of finding docs that record the detailed prediction results for different models in different tasks. Could you kindly help me have a clearer mind about that?
I would appreciate it a lot if you would like to tell me about either the logic/connection behind the filenames or where I could find the docs storing the prediction results of different models!
请问chatgpt没有提供api接口,而gpt3.5需要付费使用,你们是如何使用chatgpt进行实验的呢?是通过人工一个一个输入数据吗?
Tnank you for your interesting work.
Could you show your prompt with some few-shot learning please?
Thank you very much.
Hi, I noticed the statistics in Table 2 of your paper and found that the number of test instances for the E2E-ABSA task is inconsistent with the Sem14 test dataset. In the paper by Pontiki et al. (2014), the test size for the laptop and restaurant domains is stated as 800 sentences each. However, in Table 2, the numbers are changed to 339 and 496, and you mentioned in your paper that you used the entire test set. Therefore, I am curious about the differences here.
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