Demo Data from EDBT 2026 / reviewers in the wild / expert
Haocheng Lyu
dblp:425/8257
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 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 · 39% Multi-agent systems · 30% Trustworthy machine learning · 30% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Trustworthy machine learning
fairness and bias |
0.9 | 1 | 2025 | DiplomacyAgent: Do LLMs Balance Interests and Ethical Principles in International Events? · EMNLP 2025 |
Knowledge, reasoning and agents › Multi-agent systems
LLM-based multi-agent systems |
0.9 | 1 | 2025 | DiplomacyAgent: Do LLMs Balance Interests and Ethical Principles in International Events? · EMNLP 2025 |
Natural language and speech › Language models and text generation › evaluation of language models › reasoning evaluation
moral reasoning evaluation |
0.9 | 1 | 2025 | DiplomacyAgent: Do LLMs Balance Interests and Ethical Principles in International Events? · EMNLP 2025 |
Natural language and speech › Language models and text generation
large language model safety |
0.3 | 1 | 2025 | DiplomacyAgent: Do LLMs Balance Interests and Ethical Principles in International Events? · EMNLP 2025 |
Methods — techniques the papers use, named apart from their topics
quantitative metrics · 0.9multi-agent simulation · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | DiplomacyAgent: Do LLMs Balance Interests and Ethical Principles in International Events?abstractThe widespread deployment of large language models (LLMs) across various domains has made their safety a critical priority.Inspired by think-tank decision-making philosophy, we propose DiplomacyAgent, an LLM-based multiagent system for diplomatic position analysis.With DiplomacyAgent, we are able to systematically assess how LLMs balance "interests" against "ethical principles" when addressing various international events, hence understanding the safety implications of LLMs in diplomacy.Specifically, this will help to assess the consistency of LLM stance with widely recognized ethical standards, as well as the potential risks or ideological biases that may arise.Through integrated quantitative metrics, our research uncovers unexpected decision-making patterns in LLM responses to sensitive issues including human rights protection, environmental sustainability, regional conflicts, etc.It discloses that LLMs could exhibit a strong bias towards interests, leading to unsafe decisions that violate ethical and moral principles.Our experiment results suggest that deploying LLMs in high-stakes domains, particularly in the formulation of diplomatic policies, necessitates a comprehensive assessment of potential ethical and social implications, as well as the implementation of stringent safety protocols. Jianxiang Peng, Ling Shi 0004, Xinwei Wu 0001, Fujiang Liu, Haocheng Lyu, Deyi Xiong |
EMNLP | 6 |