Comments (5)
I think the data collected for fine-tuning in this paper does not really require to take very long since the author just mention that they prompted the larger language models for the data, it is basically knowledge distillation.
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In the langchain example, they prepend the [EXAMPLES]
which are examples of how to go about following the REact framework, this is basically few-shot learning based off prompt. This is purely prompt engineering and does not touch the weights of the model.
The method is correct. You can also use the examples for fine-tuning the llms if you have the resources (data + compute) and want better results as shown by the author of the paper in a few different datasets challenges.
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Thank you for the confirmation!
So in your paper, you are fine-tuning, which produces better output but needs a long manual data preparation period. And while fine-tuning save some token when calling API, it also increases each API call's cost. So each has pros and cons.
I will use few-shot prompt engineering as a start, and collect data for fine-tuning.
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hi @linonetwo , is there a followup question?
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I was confused about why it could do this. But I read more materials these days and I know even OpenAI doesn't know why there is the emergence.
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Related Issues (20)
- Alfworld GPT-3 Results HOT 3
- I got zero score running Webshop.ipython HOT 8
- Paper, table2 HOT 2
- Question about webshopEnv HOT 6
- Could you please tell me how to access the url in the WebShop.ipynb: http://3.83.245.205:3000 ? HOT 4
- Have you ever considered to apply ReAct prompting to numerical reasoning task? HOT 1
- Could you provide text-davinci-002 log on HotpotQA 500 (30.8EM)? HOT 1
- Potential Implementation error on Webshop
- Questions on Table 3 (AlfWorld) HOT 1
- Webshop experiment details for numbers in paper HOT 1
- Get low accuracy with GPT-3.5. HOT 12
- Question for the code HOT 1
- WEBSHOP_URL = "http://3.83.245.205:3000" 遇到一些问题 HOT 4
- Davinci-002 HOT 1
- Old or New openai version HOT 2
- [Reproducing Results] on Alfworld HOT 3
- How can I install ReAct? HOT 2
- How to finetune the small REACT model
- cot->react & react->cot HOT 2
- Jupyter output on HotpotQA HOT 1
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