Modified loss for Multi-turn conversations
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- Dominant language
- Python
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Description
Hi, upon reading the blog post of Vicuna, I see it stated that:
"Our training recipe builds on top of Stanford’s alpaca with the following improvements.
- Multi-turn conversations: We adjust the training loss to account for multi-turn conversations and compute the fine-tuning loss solely on the chatbot's output."
I wonder how impactful the loss modification is to the performance of the model? How much the difference between a chatbot model trained with and without this loss modification trick?
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Research direction
No files, tests, or entry points are named in the issue. Start by reviewing the Vicuna training recipe and its multi-turn loss description, then compare model performance with and without that loss modification; done means reporting the measured difference.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100