Demo Data from EDBT 2026 / reviewers in the wild / expert
Chao Gao 0014
dblp:86/5355-14
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2025
0000-0001-7696-7857ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Language models and text generation · 77% Multi-agent systems · 23% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Language models and text generation
large language model |
0.9 | 1 | 2025 | Beyond Demographics: Enhancing Cultural Value Survey Simulation with Multi-Stage Personality-Driven Cognitive Reasoning · EMNLP 2025 |
Computational social science and digital humanities
survey simulation |
0.9 | 1 | 2025 | Beyond Demographics: Enhancing Cultural Value Survey Simulation with Multi-Stage Personality-Driven Cognitive Reasoning · EMNLP 2025 |
Methods — techniques the papers use, named apart from their topics
personality prediction · 1.7multi-stage reasoning · 1.7cognitive imitation · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Beyond Demographics: Enhancing Cultural Value Survey Simulation with Multi-Stage Personality-Driven Cognitive ReasoningabstractIntroducing MARK, the Multi-stAge Reasoning frameworK for cultural value survey response simulation, designed to enhance the accuracy, steerability, and interpretability of large language models in this task.The system is inspired by the type dynamics theory in the MBTI psychological framework for personality research.It effectively predicts and utilizes human demographic information for simulation: life-situational stress analysis, group-level personality prediction, and self-weighted cognitive imitation.Experiments on the World Values Survey show that MARK outperforms existing baselines by 10% accuracy and reduces the divergence between model predictions and human preferences.This highlights the potential of our framework to improve zero-shot personalization and help social scientists interpret model predictions.1 Chao Gao 0014, Yong Cao 0001, Daniel Hershcovich, Jinguang Gu |
EMNLP | 3 |
| 2025 | Reviewing clinical knowledge in medical large language models: Training and beyond
Caicai Guo, Chao Gao 0014, Deyu Chen, Meng Wang 0050, Feng Gao 0003, Frank van Harmelen, Jinguang Gu |
Knowl. Based Syst. | 4 |