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Qingjing Chen

4 accepted papers

2026

LLMS ON TRIAL: Evaluating Judicial Fairness For Large Language Models

ICLR 2026poster

Large Language Models (LLMs) are increasingly used in high-stakes fields, such as law, where their decisions can directly impact people's lives. When LLMs act as judges, the ability to fairly resolve judicial issues is necessary to ensure their trustworthiness. Based on theories of judicial fairness…

Cited by 0SourcecodeScholar
2026

Self-Refine Learning in LLM Multi-Agent Systems for Legal Norm Cognition and Compliance

IJCAI 2026

As large language models (LLMs) increasingly serve as autonomous agents in social simulations, ensuring their ability to understand and comply with legal norms is essential. Yet, current LLM agents frequently exhibit reward hacking (RH) behaviors by optimizing metrics at the expense of norm adherenc

Cited by 0Scholar
2025

J&H: Evaluating the Robustness of Large Language Models Under Knowledge-Injection Attacks in Legal Domain

AAAI 2025technical

As the scale and capabilities of Large Language Models (LLMs) increase, their applications in knowledge-intensive fields such as legal domain have garnered widespread attention. However, it remains doubtful whether these LLMs make judgments based on domain knowledge for reasoning. If LLMs base their…

2025

JUREX-4E: Juridical Expert-Annotated Four-Element Knowledge Base for Legal Reasoning

EMNLP 2025

In recent years, Large Language Models (LLMs) have been widely applied to legal tasks. To enhance their understanding of legal texts and improve reasoning accuracy, a promising approach is to incorporate legal theories. One of the most widely adopted theories is the Four-Element Theory (FET), which