Question about the continued instruction-tuning phase

[Jiaxin-Wen]

In section 2.5, models are continued fine-tuned on several opensource instruction tuning datasets, which includes the training set of GSM8K and MATH.

I'm wondering after continued fine-tuning, are models evaluated still with few-shot prompting or zero-shot prompting. For example, if the model is fine-tuned on GSM8K with the following data format:

Question:\n{question}\nAnswer:\n{answer} In the inference stage, do you still incorporate multiple Q-A pairs into the input, or just the question (which is aligned with the continued fine-tuning stage).

[Jiaxin-Wen]

Moreover, I find that simply fine-tuning SOTA LMs (e.g., llama-3-8b) on the original training set of GSM8K does not lead to any improvement compared with few-shot performance.

llama-3-8b	GSM8K
few-shot prompting	55.57
fine-tuning	55.79

I would like to know if this aligns with your experiment results. If so, could you please share your data for continued instruction-tuning too? That would be really helpful to reproduce the experiment results in this paper.

[xiangyue9607]

Thanks! We used few-shot for the evaluation. You can find more details in our evaluation code and implementation details in the paper.

[Jiaxin-Wen]

will you add eos token during pre-training or continue fine-tuning?

[Jiaxin-Wen]

as all training data is in one-shot format, I'm wondering whether I should remove eos token during pre-training or fine-tuning to adapt to few-shot evaluation

[Jiaxin-Wen]

or is there any other trick that you used to adapt to few-shot evaluation?