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Title: Residual EBMs: Does Real vs. Fake Text Discrimination Generalize?

Paper content:
Energy-based models (EBMs), a.k.a.
un-normalized models, have had recent successes in continuous spaces.
However, they have not been successfully applied to model text sequences.
While decreasing the energy at training samples is straightforward, mining (negative) samples where the energy should be increased is difficult.
In part, this is because standard gradient-based methods are not readily applicable when the input is high-dimensional and discrete.
Here, we side-step this issue by generating negatives using pre-trained auto-regressive language models.
The EBM then works
in the {\em residual} of the language model; and is trained to discriminate real text from text generated by the auto-regressive models.
We  investigate the generalization ability of residual EBMs, a pre-requisite for using them in other applications.
We extensively analyze generalization for the task of classifying whether an input is machine or human generated, a natural task given the training loss and how we mine negatives.
Overall, we observe that EBMs can generalize remarkably well to changes in the architecture of the generators producing negatives.
However, EBMs exhibit more sensitivity to the training set used by such generators.
Energy-based models (EBMs) have a long history in machine learning (Hopfield, 1982; Hinton, 2002; LeCun et al., 2006) .
Their appeal stems from the minimal assumptions they make about the generative process of the data.
Unlike directed or auto-regressive models which are defined in terms of a sequence of conditional distributions, EBMs are defined in terms of a single scalar energy function, representing the joint compatibility between all input variables.
EBMs are a strict generalization of probability models, as the energy function need not be normalized or even have convergent integral.
Training an EBM consists of decreasing the energy function at the observed training data points (a.k.a. positives), while increasing it at other data points (a.k.a. negatives) (LeCun et al., 2006) .
Different learning strategies mainly differ in how negatives are mined (Ranzato et al., 2007) .
Some find negatives by gradient descent, or using Monte Carlo methods like Gibbs sampling (Welling et al., 2005) and hybrid Monte Carlo (Teh et al., 2003) , which enable the loss to approximate maximum likelihood training (Hinton, 2002) .
Other approaches instead use implicit negatives, by enforcing global constraints on the energy function, like sparsity of the internal representation (Ranzato et al., 2007) , for instance.
GANs (Goodfellow et al., 2014) can be interpreted as a particular form of EBM where the negatives are generated by a learned model.
While there are works exploring the use of EBMs for modeling images (Teh et al., 2003; Ranzato et al., 2013; Du & Mordatch, 2019) , they have not been successfully applied to text.
One reason is that text consists of sequences of discrete variables, which makes the energy function not differentiable with respect to its inputs.
Therefore, it is not possible to mine negatives using gradient-based methods.
Other approaches to mine negatives are also not immediately applicable or may be too inefficient to work at scale.
In this work, we start from the observation that current large auto-regressive locally-normalized language models are already strong , and therefore, it may be beneficial to use them to constrain the search space of negatives.
We propose to learn in the residual space of a pre-trained language model (LM), which we accomplish by using such LM to generate negatives for the EBM.
Given a dataset of positives and pre-generated negatives, the EBM can be trained using either a binary cross-entropy loss or a ranking loss, to teach the model to assign a lower energy to true human generated text than to the text generated by the pre-trained LM.
The question we ask in this work is whether such an EBM can generalize well.
Understanding this is important for two reason.
First, this generalization is a prerequisite for using residual EBMs for modeling text.
Second, in our setting, this generalization question is equivalent to the question of whether it is possible for a learned model (the energy function) to discriminate real text from text generated by an auto-regressive model.
Discriminating real vs. machine-generated text is an important task on its own that has recently gained a lot of attention (Gehrmann et al., 2019; Zellers et al., 2019) .
Our contribution is an extensive study of the generalization ability of such residual EBMs, or in other words, the generalization ability of models trained to detect real text from machine generated text.
In particular, we assess how well the energy function is robust to changes in the architecture of the generator and to changes in the data used to train the generator.
The overall finding is that the energy function is remarkably robust, and the bigger the model and the longer the generation the better its performance.
Moreover, the energy function is robust to changes in the architecture of the LM producing negatives at test time.
However, it is sensitive to the training dataset of the test generator.

assistant: text: A residual EBM for text whose formulation is equivalent to discriminating between human and machine generated text. We study its generalization behavior.