Sliding window leads to overfitting in LSTM?
Although the previous answer by @Imran is correct, I feel it necessary to add a caveat: there are applications out there where people do feed a sliding window in to an LSTM. For example, here, for framing forecasting as a supervised learning problem. 

If your data are not very rich, then you may find that any LSTM at all overfits. There are a lot of parameters in an LSTM (in fact, there are $4(mn + n^2 + n)$ parameters, where $m$ is the input length and $n$ is the output length, according to this post). 

Since LSTMs do not require fixed size input, they can find the optimal lookback number on their own. However, if you've done a prior autoregressive analysis and decided that, for example, the current time step is most correlated with the 10th previous time step, and not correlated with the 11th or any time steps further in the past, then you could perhaps save yourself some training time by feeding in fixed-length sequences. However, that kind of defeats the purpose of an LSTM. 

If your data are not rich enough for an LSTM, I would recommend trying something much simpler, like an autoregressive model, and working your way up.

EDIT (responding to a comment):

Overlapping sequences are used as input, especially when the sequence is very long (although, of course, 'long' is relative). Although LSTMs are better than a vanilla RNN for long sequences, they can still have some trouble remembering time steps from the beginning of a sequence if the sequence is very long. That led to things like the bidirectional LSTM, which reads the sequence forwards and backwards, improving the exposure of the network to the beginning and end of each input sequence. The principle is the same with overlapping sequences, although I would argue that overlapping sequences is more intuitive.