HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving
Large computer-understandable proofs consist of millions of intermediate logical steps. The vast majority of such steps originate from manually selected and manually guided heuristics applied to intermediate goals. So far, machine learning has generally not been used to filter or generate these steps. In this paper, we introduce a new dataset based on Higher-Order Logic (HOL) proofs, for the purpose of developing new machine learning-based theorem-proving strategies. We make this dataset publicly available under the BSD license. We propose various machine learning tasks that can be performed on this dataset, and discuss their significance for theorem proving. We also benchmark a set of simple baseline machine learning models suited for the tasks (including logistic regression convolutional neural networks and recurrent neural networks). The results of our baseline models show the promise of applying machine learning to HOL theorem proving.
The authors present a dataset extraction method, dataset and first interesting results for machine-learning supported higher order logic theorem proving. The experimental results are impressively good for a first baseline and with an accuracy higher than 0.83 in relevance classification a lot better than chance, and encourage future research in this direction. The paper is well-written in terms of presentation and argumentation and leaves little room for criticism. The related work seems to be well-covered, though I have to note that I am not an expert for automated theorem proving.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving
Large computer-understandable proofs consist of millions of intermediate logical steps. The vast majority of such steps originate from manually selected and manually guided heuristics applied to intermediate goals. So far, machine learning has generally not been used to filter or generate these steps. In this paper, we introduce a new dataset based on Higher-Order Logic (HOL) proofs, for the purpose of developing new machine learning-based theorem-proving strategies. We make this dataset publicly available under the BSD license. We propose various machine learning tasks that can be performed on this dataset, and discuss their significance for theorem proving. We also benchmark a set of simple baseline machine learning models suited for the tasks (including logistic regression convolutional neural networks and recurrent neural networks). The results of our baseline models show the promise of applying machine learning to HOL theorem proving.
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 present a dataset extraction method, dataset and first interesting results for machine-learning supported higher order logic theorem proving. The experimental results are impressively good for a first baseline and with an accuracy higher than 0.83 in relevance classification a lot better than chance, and encourage future research in this direction. The paper is well-written in terms of presentation and argumentation and leaves little room for criticism. The related work seems to be well-covered, though I have to note that I am not an expert for automated theorem proving.
assistant
The authors present a dataset extraction method, dataset and first interesting results for machine-learning supported higher order logic theorem proving. The experimental results are impressively good for a first baseline and with an accuracy higher than 0.83 in relevance classification a lot better than chance, and encourage future research in this direction. The paper is well-written in terms of presentation and argumentation and leaves little room for criticism. The related work seems to be well-covered, though I have to note that I am not an expert for automated theorem proving.