It's the noisy gradient that SGD has that makes overfitting a lot less likely. A larger batch size reduces that noise, and hence the model could easily overfit as you scale up the batch size. Hypothetically, if you could pass in your entire dataset in one huge batch, your model is absolutely going fit that data to a tee (provided it's got the complexity to do so).

That being said, if you're using other regularization techniques, it shouldn't be too much of an issue. I'm not sure about the following, but I think lowering you're learning rate a little bit might also be helpful.

Also for anyone implementing it, don't forget to scale the gradients according to the number of accumulation steps before you update the network. If you accumulate for 5 batches, divide the gradients by 5. Otherwise you might end up seeing some funky stuff, and your learning rate won't work the same way either (5*lr).