4 papers
Hint-Guided Diversified Policy Optimization for LLM Reasoning
Zhiyu Cao, Kaixin Wu, Mingjie Zhong +4
Recent developments in Large Language Models (LLMs) have showcased impressive reasoning capabilities, with Reinforcement Learning with Verifiable Rewards (RLVR) being a promising e…
GAST: Gradient-aligned Sparse Tuning of Large Language Models with Data-layer Selection
Kai Yao, Zhenghan Song, Kaixin Wu +5
Parameter-Efficient Fine-Tuning (PEFT) has become a key strategy for adapting large language models, with recent advances in sparse tuning reducing overhead by selectively updating…
Alleviating LLM-based Generative Retrieval Hallucination in Alipay Search
Yedan Shen, Kaixin Wu, Yuechen Ding +6
Generative retrieval (GR) has revolutionized document retrieval with the advent of large language models (LLMs), and LLM-based GR is gradually being adopted by the industry. Despit…
Towards Boosting LLMs-driven Relevance Modeling with Progressive Retrieved Behavior-augmented Prompting
Zeyuan Chen, Haiyan Wu, Kaixin Wu +5
Relevance modeling is a critical component for enhancing user experience in search engines, with the primary objective of identifying items that align with users' queries. Traditio…