6 papers
U-CAN: Utility-Aware Contrastive Attenuation for Efficient Unlearning in Generative Recommendation
Zezheng Wu, Rui Wang, Xinghe Cheng +4
Generative Recommendation (GenRec) typically leverages Large Language Models (LLMs) to redefine personalization as an instruction-driven sequence generation task. However, fine-tun…
Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction
Xiaonan Jing, Gongqing Wu, Xingrui Zhuo +2
Open-domain Relational Triplet Extraction (ORTE) aims to mine structured knowledge without predefined relation schemas. Large Language Models (LLMs) have advanced ORTE toward a pro…
LexGenius: An Expert-Level Benchmark for Large Language Models in Legal General Intelligence
Wenjin Liu, Haoran Luo, Xin Feng +6
Legal general intelligence (GI) refers to artificial intelligence (AI) that encompasses legal understanding, reasoning, and decision-making, simulating the expertise of legal exper…
Prompt-R1: Collaborative Automatic Prompting Framework via End-to-end Reinforcement Learning
Wenjin Liu, Haoran Luo, Xueyuan Lin +5
Recently, advanced large language models (LLMs) have emerged at an increasingly rapid pace. However, when faced with complex problems, most users are often unable to provide accura…
Accelerate Scaling of LLM Finetuning via Quantifying the Coverage and Depth of Instruction Set
Chengwei Wu, Li Du, Hanyu Zhao +4
Scaling the amount of data used for supervied fine-tuning(SFT) does not guarantee the proportional gains in model performance, highlighting a critical need to understand what makes…
A Large-Scale Chinese Knowledge Graph-Text Alignment Dataset for Benchmarking Knowledge-Grounded LLMs
Chengwei Wu, Xingrui Zhuo, Mingyang Gao +3
Reliable evaluation of knowledge-grounded Large Language Models (LLMs) in Chinese requires resources that explicitly align Chinese-language text with verifiable Knowledge Graph (KG…