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 paper discusses training an energy-based discriminator to discriminates the generated examples from a language model from human-written sentences. To do this, it assigns lower energy value to the real text and higher energy values to the generated text, by optimizing binary cross-entropy. The motivation of the paper claims that the authors solve (or at least move toward solving) the problem of sampling from energy-based models for high-dimensional sequence problems; however, I do not see any contribution towards that. The importance of having a well-trained energy-based model is its modeling capability, which also enables us to sample from it (that is done using gradient descent and Langevin dynamic for modeling images). But here, the energy-based model has no effect on the quality of samples from the auto-regressive model, so it does not give us a better model for text. It would be similar to EBGAN (Zhao et al., 2017), but only training the energy discriminator and keeping the generator as is (here a pretrained LM). This work would become an interesting and influential work if you can update the generator parameters from the feedback of the energy-based model, so the generator works as a sampling process for the energy-based model (which was what I expected while I was reading the first part of the paper).
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 paper discusses training an energy-based discriminator to discriminates the generated examples from a language model from human-written sentences. To do this, it assigns lower energy value to the real text and higher energy values to the generated text, by optimizing binary cross-entropy. The motivation of the paper claims that the authors solve (or at least move toward solving) the problem of sampling from energy-based models for high-dimensional sequence problems; however, I do not see any contribution towards that. The importance of having a well-trained energy-based model is its modeling capability, which also enables us to sample from it (that is done using gradient descent and Langevin dynamic for modeling images). But here, the energy-based model has no effect on the quality of samples from the auto-regressive model, so it does not give us a better model for text. It would be similar to EBGAN (Zhao et al., 2017), but only training the energy discriminator and keeping the generator as is (here a pretrained LM). This work would become an interesting and influential work if you can update the generator parameters from the feedback of the energy-based model, so the generator works as a sampling process for the energy-based model (which was what I expected while I was reading the first part of the paper).
assistant
The paper discusses training an energy-based discriminator to discriminates the generated examples from a language model from human-written sentences. To do this, it assigns lower energy value to the real text and higher energy values to the generated text, by optimizing binary cross-entropy. The motivation of the paper claims that the authors solve (or at least move toward solving) the problem of sampling from energy-based models for high-dimensional sequence problems; however, I do not see any contribution towards that. The importance of having a well-trained energy-based model is its modeling capability, which also enables us to sample from it (that is done using gradient descent and Langevin dynamic for modeling images). But here, the energy-based model has no effect on the quality of samples from the auto-regressive model, so it does not give us a better model for text. It would be similar to EBGAN (Zhao et al., 2017), but only training the energy discriminator and keeping the generator as is (here a pretrained LM). This work would become an interesting and influential work if you can update the generator parameters from the feedback of the energy-based model, so the generator works as a sampling process for the energy-based model (which was what I expected while I was reading the first part of the paper).