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Chao Gao 0014

dblp:86/5355-14 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Natural language and speech › Language models and text generation
large language model
0.912025
Beyond Demographics: Enhancing Cultural Value Survey Simulation with Multi-Stage Personality-Driven Cognitive Reasoning · EMNLP 2025
Computational social science and digital humanities
survey simulation
0.912025
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
YearPublicationVenuePosition
2025 Beyond Demographics: Enhancing Cultural Value Survey Simulation with Multi-Stage Personality-Driven Cognitive Reasoning
abstract
Introducing 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
EMNLP3
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