Yangshijie Zhang

Although the effectiveness of Large Language Models as judges has been validated, their performance remains limited in open-ended tasks, particularly in story evaluation. Accurate story evaluation is crucial not only for assisting human quality judgment but also for providing reward signals to gu...

Reinforcement learning for open-ended text generation is constrained by the lack of verifiable rewards, necessitating reliance on judge models that require either annotated data or powerful closed-source models. Inspired by recent work on unsupervised reinforcement learning for mathematical reaso...

Existing In-context Learning (ICL) typically assumes the retrieval dataset contains demonstrations for all output label spaces. However, in real-world scenarios, delays in dataset updates or incomplete data annotation may result in the retrieval dataset containing labeled demonstrations for only...

Standard in-context learning (ICL) assumes identical output spaces between test and retrieval datasets (fully aligned). However, in practice, these datasets can be fully aligned, partially aligned, or fully disjoint in label space (Output space), forming an information continuum from rich to scar...