RelevAI-Reviewer: A Benchmark on AI Reviewers for Survey Paper Relevance
- URL: http://arxiv.org/abs/2406.10294v1
- Date: Thu, 13 Jun 2024 06:42:32 GMT
- Title: RelevAI-Reviewer: A Benchmark on AI Reviewers for Survey Paper Relevance
- Authors: Paulo Henrique Couto, Quang Phuoc Ho, Nageeta Kumari, Benedictus Kent Rachmat, Thanh Gia Hieu Khuong, Ihsan Ullah, Lisheng Sun-Hosoya,
- Abstract summary: 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.
- Score: 0.8089605035945486
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent advancements in Artificial Intelligence (AI), particularly the widespread adoption of Large Language Models (LLMs), have significantly enhanced text analysis capabilities. This technological evolution offers considerable promise for automating the review of scientific papers, a task traditionally managed through peer review by fellow researchers. Despite its critical role in maintaining research quality, the conventional peer-review process is often slow and subject to biases, potentially impeding the swift propagation of scientific knowledge. In this paper, we propose RelevAI-Reviewer, an automatic system that conceptualizes the task of survey paper review as a classification problem, aimed at assessing the relevance of a paper in relation to a specified prompt, analogous to a "call for papers". To address this, 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. The objective is to develop a machine learning (ML) model capable of determining the relevance of each paper and identifying the most pertinent one. We explore various baseline approaches, including traditional ML classifiers like Support Vector Machine (SVM) and advanced language models such as BERT. Preliminary findings indicate that the BERT-based end-to-end classifier surpasses other conventional ML methods in performance. We present this problem as a public challenge to foster engagement and interest in this area of research.
Related papers
- Automating Bibliometric Analysis with Sentence Transformers and Retrieval-Augmented Generation (RAG): A Pilot Study in Semantic and Contextual Search for Customized Literature Characterization for High-Impact Urban Research [2.1728621449144763]
Bibliometric analysis is essential for understanding research trends, scope, and impact in urban science.
Traditional methods, relying on keyword searches, often fail to uncover valuable insights not explicitly stated in article titles or keywords.
We leverage Generative AI models, specifically transformers and Retrieval-Augmented Generation (RAG), to automate and enhance bibliometric analysis.
arXiv Detail & Related papers (2024-10-08T05:13:27Z) - ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models [56.08917291606421]
ResearchAgent is an AI-based system for ideation and operationalization of novel work.
ResearchAgent automatically defines novel problems, proposes methods and designs experiments, while iteratively refining them.
We experimentally validate our ResearchAgent on scientific publications across multiple disciplines.
arXiv Detail & Related papers (2024-04-11T13:36:29Z) - System for systematic literature review using multiple AI agents:
Concept and an empirical evaluation [5.194208843843004]
We introduce a novel multi-AI agent model designed to fully automate the process of conducting Systematic Literature Reviews.
The model operates through a user-friendly interface where researchers input their topic.
It generates a search string used to retrieve relevant academic papers.
The model then autonomously summarizes the abstracts of these papers.
arXiv Detail & Related papers (2024-03-13T10:27:52Z) - A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence [55.33653554387953]
Pattern Analysis and Machine Intelligence (PAMI) has led to numerous literature reviews aimed at collecting and fragmented information.
This paper presents a thorough analysis of these literature reviews within the PAMI field.
We try to address three core research questions: (1) What are the prevalent structural and statistical characteristics of PAMI literature reviews; (2) What strategies can researchers employ to efficiently navigate the growing corpus of reviews; and (3) What are the advantages and limitations of AI-generated reviews compared to human-authored ones.
arXiv Detail & Related papers (2024-02-20T11:28:50Z) - Chain-of-Factors Paper-Reviewer Matching [32.86512592730291]
We propose a unified model for paper-reviewer matching that jointly considers semantic, topic, and citation factors.
We demonstrate the effectiveness of our proposed Chain-of-Factors model in comparison with state-of-the-art paper-reviewer matching methods and scientific pre-trained language models.
arXiv Detail & Related papers (2023-10-23T01:29:18Z) - Generative Judge for Evaluating Alignment [84.09815387884753]
We propose a generative judge with 13B parameters, Auto-J, designed to address these challenges.
Our model is trained on user queries and LLM-generated responses under massive real-world scenarios.
Experimentally, Auto-J outperforms a series of strong competitors, including both open-source and closed-source models.
arXiv Detail & Related papers (2023-10-09T07:27:15Z) - 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) - Toward Educator-focused Automated Scoring Systems for Reading and
Writing [0.0]
This paper addresses the challenges of data and label availability, authentic and extended writing, domain scoring, prompt and source variety, and transfer learning.
It employs techniques that preserve essay length as an important feature without increasing model training costs.
arXiv Detail & Related papers (2021-12-22T15:44:30Z) - Mining Implicit Relevance Feedback from User Behavior for Web Question
Answering [92.45607094299181]
We make the first study to explore the correlation between user behavior and passage relevance.
Our approach significantly improves the accuracy of passage ranking without extra human labeled data.
In practice, this work has proved effective to substantially reduce the human labeling cost for the QA service in a global commercial search engine.
arXiv Detail & Related papers (2020-06-13T07:02:08Z) - Recognizing Families In the Wild: White Paper for the 4th Edition Data
Challenge [91.55319616114943]
This paper summarizes the supported tasks (i.e., kinship verification, tri-subject verification, and search & retrieval of missing children) in the Recognizing Families In the Wild (RFIW) evaluation.
The purpose of this paper is to describe the 2020 RFIW challenge, end-to-end, along with forecasts in promising future directions.
arXiv Detail & Related papers (2020-02-15T02:22:42Z)
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.