Unveiling the Sentinels: Assessing AI Performance in Cybersecurity Peer
Review
- URL: http://arxiv.org/abs/2309.05457v1
- Date: Mon, 11 Sep 2023 13:51:40 GMT
- Title: Unveiling the Sentinels: Assessing AI Performance in Cybersecurity Peer
Review
- Authors: Liang Niu, Nian Xue, Christina P\"opper
- Abstract summary: In the field of cybersecurity, the practice of double-blind peer review is the de-facto standard.
This paper touches on the holy grail of peer reviewing and aims to shed light on the performance of AI in reviewing for academic security conferences.
We investigate the predictability of reviewing outcomes by comparing the results obtained from human reviewers and machine-learning models.
- Score: 4.081120388114928
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Peer review is the method employed by the scientific community for evaluating
research advancements. In the field of cybersecurity, the practice of
double-blind peer review is the de-facto standard. This paper touches on the
holy grail of peer reviewing and aims to shed light on the performance of AI in
reviewing for academic security conferences. Specifically, we investigate the
predictability of reviewing outcomes by comparing the results obtained from
human reviewers and machine-learning models. To facilitate our study, we
construct a comprehensive dataset by collecting thousands of papers from
renowned computer science conferences and the arXiv preprint website. Based on
the collected data, we evaluate the prediction capabilities of ChatGPT and a
two-stage classification approach based on the Doc2Vec model with various
classifiers. Our experimental evaluation of review outcome prediction using the
Doc2Vec-based approach performs significantly better than the ChatGPT and
achieves an accuracy of over 90%. While analyzing the experimental results, we
identify the potential advantages and limitations of the tested ML models. We
explore areas within the paper-reviewing process that can benefit from
automated support approaches, while also recognizing the irreplaceable role of
human intellect in certain aspects that cannot be matched by state-of-the-art
AI techniques.
Related papers
- Deep Transfer Learning Based Peer Review Aggregation and Meta-review Generation for Scientific Articles [2.0778556166772986]
We address two peer review aggregation challenges: paper acceptance decision-making and meta-review generation.
Firstly, we propose to automate the process of acceptance decision prediction by applying traditional machine learning algorithms.
For the meta-review generation, we propose a transfer learning model based on the T5 model.
arXiv Detail & Related papers (2024-10-05T15:40:37Z) - Analysis of the ICML 2023 Ranking Data: Can Authors' Opinions of Their Own Papers Assist Peer Review in Machine Learning? [52.00419656272129]
We conducted an experiment during the 2023 International Conference on Machine Learning (ICML)
We received 1,342 rankings, each from a distinct author, pertaining to 2,592 submissions.
We focus on the Isotonic Mechanism, which calibrates raw review scores using author-provided rankings.
arXiv Detail & Related papers (2024-08-24T01:51:23Z) - Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction [54.23208041792073]
Aspect Sentiment Quad Prediction (ASQP) aims to predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review.
A key challenge in the ASQP task is the scarcity of labeled data, which limits the performance of existing methods.
We propose a self-training framework with a pseudo-label scorer, wherein a scorer assesses the match between reviews and their pseudo-labels.
arXiv Detail & Related papers (2024-06-26T05:30:21Z) - RelevAI-Reviewer: A Benchmark on AI Reviewers for Survey Paper Relevance [0.8089605035945486]
We propose RelevAI-Reviewer, an automatic system that conceptualizes the task of survey paper review as a classification problem.
We introduce a novel dataset comprised of 25,164 instances. Each instance contains one prompt and four candidate papers, each varying in relevance to the prompt.
We develop a machine learning (ML) model capable of determining the relevance of each paper and identifying the most pertinent one.
arXiv Detail & Related papers (2024-06-13T06:42:32Z) - A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence [58.6354685593418]
This paper proposes several article-level, field-normalized, and large language model-empowered bibliometric indicators to evaluate reviews.
The newly emerging AI-generated literature reviews are also appraised.
This work offers insights into the current challenges of literature reviews and envisions future directions for their development.
arXiv Detail & Related papers (2024-02-20T11:28:50Z) - GPT4 is Slightly Helpful for Peer-Review Assistance: A Pilot Study [0.0]
GPT4 was developed to assist in the peer-review process.
By comparing reviews generated by both human reviewers and GPT models for academic papers submitted to a major machine learning conference, we provide initial evidence that artificial intelligence can contribute effectively to the peer-review process.
arXiv Detail & Related papers (2023-06-16T23:11:06Z) - Investigating Fairness Disparities in Peer Review: A Language Model
Enhanced Approach [77.61131357420201]
We conduct a thorough and rigorous study on fairness disparities in peer review with the help of large language models (LMs)
We collect, assemble, and maintain a comprehensive relational database for the International Conference on Learning Representations (ICLR) conference from 2017 to date.
We postulate and study fairness disparities on multiple protective attributes of interest, including author gender, geography, author, and institutional prestige.
arXiv Detail & Related papers (2022-11-07T16:19:42Z) - Ranking Scientific Papers Using Preference Learning [48.78161994501516]
We cast it as a paper ranking problem based on peer review texts and reviewer scores.
We introduce a novel, multi-faceted generic evaluation framework for making final decisions based on peer reviews.
arXiv Detail & Related papers (2021-09-02T19:41:47Z) - Hierarchical Bi-Directional Self-Attention Networks for Paper Review
Rating Recommendation [81.55533657694016]
We propose a Hierarchical bi-directional self-attention Network framework (HabNet) for paper review rating prediction and recommendation.
Specifically, we leverage the hierarchical structure of the paper reviews with three levels of encoders: sentence encoder (level one), intra-review encoder (level two) and inter-review encoder (level three)
We are able to identify useful predictors to make the final acceptance decision, as well as to help discover the inconsistency between numerical review ratings and text sentiment conveyed by reviewers.
arXiv Detail & Related papers (2020-11-02T08:07:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.