You might want to have a look at the implementation of LDA in Mallet, which can do hyperparameter optimization as part of the training. Mallet also uses asymmetric priors by default, which according to this paper, leads to the model being much more robust against setting the number of topics too high. In practice this means you don't have to specify the hyperparameters, and can set number of topics pretty high without negatively affecting results.
In my experience hyperparameter optimization and asymmetric priors gave significantly better topics than without it, but I haven't tried the Matlab Topic Modelling toolkit.
Not sure if you're still looking for an answer, but I'll chime in anyways.
In short: this "technically" works fine. You're essentially just supplementing your documents to have a big list of all contiguous pairs of words (or n-tuples for general n-grams) at the end.
The topic model will think that new_york has nothing to do with new or york. Although you might notice that these singular pieces of phrases and the phrases show up together in the topics often... after all topic models find groups of words that cooccur in the same documents often!
Complications:
Now the only real issue with this is that your model may have a harder time fitting to your data if they're supplemented with n-grams. Why? Because you will have a much larger vocabulary to work with, and you'll need to jack up the number of topics to get a comparable fit.
This of course will make your model training much slower.
If you prune your vocabulary, you'll cut out more rare words and keep more phrases, essentially artificially inflating the topic proportions of your documents.
You might benefit from judiciously choosing the phrases in your vocabulary instead of just using ngrams (actually probably all text analysis can reap this same benefit). For some ideas, you might find this useful.
http://www.mimno.org/articles/phrases/
Background on topic models that may give the above appropriate context:
LDA is simply finding a mixture of distributions of terms for each document that leads to the (approximate) maximal value under the posterior probability of the document-topic proportions and the topic-word proportions (outside of the documents). Mathematically this looks like:
$logp(\beta_{1:K}, \theta_{1,D}, z_{1,DN}, w_{1,DN}) = \\
\Sigma_k^Klogp(\beta_k) + \Sigma_d^D[logp(\theta_d) + \Sigma_i^Nlogp(z_{i,d} | \theta_d) + \Sigma_i^Nlogp(w_{i,d}|z_{i,d},\beta_{1:K})] $
(Note that working in the logspace lets us separate the multiplicative factors into sums for convenience.)
The important factors in this are the last two terms, because chances are your documents have a reasonable number of words, and so the $ND$ terms there will dominate the function.
So let's look at those two terms more closely:
$logp(z_{i,d} | \theta_d)$ is just the $logp$ of that word getting topic assignment $z_i$ under the topic distribution for that document.
This term will be high if the document has very few topics.
The other dominating term in that big equation is $logp(w_{i,d}|z_{i,d},\beta_{1:K})$. This term simply asks for for the probability of that word under the topic it has been assigned to by $z_{i,d}$. It will be high when that word is very likely in the topic.
These two terms are at odds with each other. One wants few topics per document so all of the topic assignments have high likelihood, while the other wants only a few words per topic so those chosen few words can have high likelihood.
Best Answer
"Distributed algorithms for topic models" by Newman, D. and Asuncion, A. and Smyth, P. and Welling, M. gives an auxiliary variable sampling method for hyperparameters. These methods are related to sampling schemes for Hierarchical Dirichlet Process parameters. It doesn't appear that Hannah Wallach includes this method in her dissertation.
Also, "On Smoothing and Inference for Topic Models" by Teh et. al. has an interesting discussion on the role of hyperparameters in LDA.