SciEvo: A 2 Million, 30-Year Cross-disciplinary Dataset for Temporal Scientometric Analysis
SciEvo is a comprehensive dataset containing 2 million+ papers spanning 30 years (1995-2024) for temporal scientometric analysis.
Mar 3, 2025
AgentReview: Exploring Peer Review Dynamics with LLM Agents
Peer review is fundamental to the integrity and advancement of scientific publication. Traditional methods of peer review analyses often rely on exploration and statistics of existing peer review data...
Nov 12, 2024
Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluation
We address fairness issues in graph anomaly detection, providing benchmark datasets and comprehensive evaluation frameworks for fair anomaly detection on graphs....
Jul 4, 2024
CompeteAI: Understanding the Competition Dynamics of Large Language Model-based Agents
This work studies the competition dynamics among LLM-based agents, revealing emergent behaviors and strategic patterns in multi-agent systems....
Apr 30, 2024
Prototypical Reward Network for Data-Efficient RLHF
We propose a prototypical reward network that enables data-efficient reinforcement learning from human feedback (RLHF) for large language models....
Jan 1, 2024
MM-SOC: Benchmarking Multimodal Large Language Models in Social Media Platforms
Social media platforms are hubs for multimodal information exchange, encompassing text, images, and videos, making it challenging for machines to comprehensively understand the information. Multimodal...
Jan 1, 2024
Better to Ask in English: Cross-Lingual Evaluation of Large Language Models for Healthcare Queries
We present a framework and benchmark to evaluate LLMs' multilingual capabilities in healthcare queries, revealing significant performance gaps across languages and providing insights for improving hea...
Jan 1, 2024
Semi-Offline Reinforcement Learning for Optimized Text Generation
We propose a semi-offline reinforcement learning approach for optimizing text generation in language models, balancing exploration and exploitation effectively....
May 1, 2023
Prototypical Fine-Tuning: Towards Robust Performance under Varying Data Sizes
We propose prototypical fine-tuning, a novel framework for fine-tuning pretrained language models that maintains robust performance across varying data sizes.
Jan 1, 2023
Predicting Information Pathways Across Online Communities
We develop methods to predict how information spreads across different online communities, revealing patterns in cross-platform information diffusion....
Jan 1, 2023