Residual EBMs: Does Real vs. Fake Text Discrimination Generalize?
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.
The authors consider energy-based learning for discriminating real vs machine generated text. They explore the approach of using a generative text model to generate machine generated training examples. They then consider generalization when the generative model in test time is different from the generative model used for training, as well as if the training data changes to a different corpus. They observe that their model is reasonably able to generalize when the generative model or training data is changed. The experiments in this work seem fine. My main issue is with the theory, specifically if their approach to using an energy function is truly different from supervised learning. It is not really clear why an energy model is useful for the tasks they consider in the paper, as it essentially seems to be a binary classification problem. Additionally, is it fair to say that the BCE loss used is essentially the classic supervised loss function, modulo using the most offending negative? If we model the probability the text is real as exp(-E(x)) / (exp(-E(x)) + 1), then we get the classic supervised loss function (again modulo using the most offending negative). To me, an energy based approach would be to model P(x) = exp(-E(x)) / Z, where Z is a normalization constant. I would calculate the log likelihood as -E(x) - log(Z), which agrees with LeCun, 2006 and Du and Mordatch, 2019. The authors need to do a better job explaining why they consider this an energy based approach and relate it to more typical energy based approaches. Additionally, a bit of text describing why using the most offending negative would be helpful. An additional experiment that would be interesting is to train the model using many different generative text models and test on an unused generative text model. I would also like to see a more formal definition of *residual*.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Residual EBMs: Does Real vs. Fake Text Discrimination Generalize?
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.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The authors consider energy-based learning for discriminating real vs machine generated text. They explore the approach of using a generative text model to generate machine generated training examples. They then consider generalization when the generative model in test time is different from the generative model used for training, as well as if the training data changes to a different corpus. They observe that their model is reasonably able to generalize when the generative model or training data is changed. The experiments in this work seem fine. My main issue is with the theory, specifically if their approach to using an energy function is truly different from supervised learning. It is not really clear why an energy model is useful for the tasks they consider in the paper, as it essentially seems to be a binary classification problem. Additionally, is it fair to say that the BCE loss used is essentially the classic supervised loss function, modulo using the most offending negative? If we model the probability the text is real as exp(-E(x)) / (exp(-E(x)) + 1), then we get the classic supervised loss function (again modulo using the most offending negative). To me, an energy based approach would be to model P(x) = exp(-E(x)) / Z, where Z is a normalization constant. I would calculate the log likelihood as -E(x) - log(Z), which agrees with LeCun, 2006 and Du and Mordatch, 2019. The authors need to do a better job explaining why they consider this an energy based approach and relate it to more typical energy based approaches. Additionally, a bit of text describing why using the most offending negative would be helpful. An additional experiment that would be interesting is to train the model using many different generative text models and test on an unused generative text model. I would also like to see a more formal definition of *residual*.
assistant
The authors consider energy-based learning for discriminating real vs machine generated text. They explore the approach of using a generative text model to generate machine generated training examples. They then consider generalization when the generative model in test time is different from the generative model used for training, as well as if the training data changes to a different corpus. They observe that their model is reasonably able to generalize when the generative model or training data is changed. The experiments in this work seem fine. My main issue is with the theory, specifically if their approach to using an energy function is truly different from supervised learning. It is not really clear why an energy model is useful for the tasks they consider in the paper, as it essentially seems to be a binary classification problem. Additionally, is it fair to say that the BCE loss used is essentially the classic supervised loss function, modulo using the most offending negative? If we model the probability the text is real as exp(-E(x)) / (exp(-E(x)) + 1), then we get the classic supervised loss function (again modulo using the most offending negative). To me, an energy based approach would be to model P(x) = exp(-E(x)) / Z, where Z is a normalization constant. I would calculate the log likelihood as -E(x) - log(Z), which agrees with LeCun, 2006 and Du and Mordatch, 2019. The authors need to do a better job explaining why they consider this an energy based approach and relate it to more typical energy based approaches. Additionally, a bit of text describing why using the most offending negative would be helpful. An additional experiment that would be interesting is to train the model using many different generative text models and test on an unused generative text model. I would also like to see a more formal definition of *residual*.