6 papers
TSPORec: Token Selection via Preference Optimization for LLM-Based Sequential Recommendation
Wenqiao Zhu, Chao Xu, Haipang Wu +1
Large Language Models (LLMs) have emerged as powerful tools for improving recommendation systems. The effectiveness of LLMs arises from their ability to harness rich textual inform…
CARFT: Boosting LLM Reasoning via Contrastive Learning with Annotated Chain-of-Thought-based Reinforced Fine-Tuning
Wenqiao Zhu, Ji Liu, Rongjuncheng Zhang +2
Reasoning capability plays a significantly critical role in the the broad applications of Large Language Models (LLMs). To enhance the reasoning performance of LLMs, diverse Reinfo…
BizFinBench: A Business-Driven Real-World Financial Benchmark for Evaluating LLMs
Guilong Lu, Xuntao Guo, Rongjunchen Zhang +2
Large language models excel in general tasks, yet assessing their reliability in logic-heavy, precision-critical domains like finance, law, and healthcare remains challenging. To a…
SGDPO: Self-Guided Direct Preference Optimization for Language Model Alignment
Wenqiao Zhu, Ji Liu, Lulu Wang +2
Direct Preference Optimization (DPO) is broadly utilized for aligning Large Language Models (LLMs) with human values because of its flexibility. Despite its effectiveness, it has b…
PSC: Extending Context Window of Large Language Models via Phase Shift Calibration
Wenqiao Zhu, Chao Xu, Lulu Wang +1
Rotary Position Embedding (RoPE) is an efficient position encoding approach and is widely utilized in numerous large language models (LLMs). Recently, a lot of methods have been pu…
Addressing Cold-start Problem in Click-Through Rate Prediction via Supervised Diffusion Modeling
Wenqiao Zhu, Lulu Wang, Jun Wu
Predicting Click-Through Rates is a crucial function within recommendation and advertising platforms, as the output of CTR prediction determines the order of items shown to users.…