5 papers
Led to Mislead: Adversarial Content Injection for Attacks on Neural Ranking Models
Amin Bigdeli, Amir Khosrojerdi, Radin Hamidi Rad +3
Neural Ranking Models (NRMs) are central to modern information retrieval but remain highly vulnerable to adversarial manipulation. Existing attacks often rely on heuristics or surr…
PLawBench: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice
Yuzhen Shi, Huanghai Liu, Yiran Hu +27
As large language models (LLMs) are increasingly applied to legal domain-specific tasks, evaluating their ability to perform legal work in real-world settings has become essential.…
Evaluation of Large Language Models in Legal Applications: Challenges, Methods, and Future Directions
Yiran Hu, Huanghai Liu, Chong Wang +15
Large language models (LLMs) are being increasingly integrated into legal applications, including judicial decision support, legal practice assistance, and public-facing legal serv…
LLMs on Trial: Evaluating Judicial Fairness for Large Language Models
Yiran Hu, Zongyue Xue, Haitao Li +10
Large Language Models (LLMs) are increasingly used in high-stakes fields where their decisions impact rights and equity. However, LLMs' judicial fairness and implications for socia…
J&H: Evaluating the Robustness of Large Language Models Under Knowledge-Injection Attacks in Legal Domain
Yiran Hu, Huanghai Liu, Qingjing Chen +5
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. Ho…