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Even GPT-4, a well-performing model, can generate outdated code due to outdated training data. However, LLMs have a strong language understanding ability, which can be used to correct these errors through prompts.
To enable LLM to use command-line tools and libraries that are not covered by the training data. This includes two cases:
Tools and code repositories don't being included in the training data at all.
The model is trained on data from outdated tools or libraries, which means that the model cannot use them right
To prevent LLM from repeating the same mistakes. When a LLM uses an incorrect code usage or tool, it will always repeat the incorrect code and then recall the correct result through long-term memory before executing the code. However, this can be improved by using short-term memory to store the correct tool or code usage in the instructions.
Background and Why
Even GPT-4, a well-performing model, can generate outdated code due to outdated training data. However, LLMs have a strong language understanding ability, which can be used to correct these errors through prompts.
Desgin
Other Memory Desgin
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