5 papers
CoLD: Counterfactually-Guided Length Debiasing for Process Reward Models in Mathematical Reasoning
Congmin Zheng, Jiachen Zhu, Jianghao Lin +6
Process Reward Models (PRMs) play a central role in evaluating and guiding multi-step reasoning in large language models (LLMs), especially for mathematical problem solving. Howeve…
SwiReasoning: Switch-Thinking in Latent and Explicit for Pareto-Superior Reasoning LLMs
Dachuan Shi, Abedelkadir Asi, Keying Li +4
Recent work shows that, beyond discrete reasoning through explicit chain-of-thought steps, which are limited by the boundaries of natural languages, large language models (LLMs) ca…
The Role of Diversity in In-Context Learning for Large Language Models
Wenyang Xiao, Haoyu Zhao, Lingxiao Huang
In-context learning (ICL) is a crucial capability of current large language models (LLMs), where the selection of examples plays a key role in performance. While most existing appr…
Interpretable Credit Default Prediction with Ensemble Learning and SHAP
Shiqi Yang, Ziyi Huang, Wengran Xiao +1
This study focuses on the problem of credit default prediction, builds a modeling framework based on machine learning, and conducts comparative experiments on a variety of mainstre…
How Good are LLMs at Relation Extraction under Low-Resource Scenario? Comprehensive Evaluation
Dawulie Jinensibieke, Mieradilijiang Maimaiti, Wentao Xiao +2
Relation Extraction (RE) serves as a crucial technology for transforming unstructured text into structured information, especially within the framework of Knowledge Graph developme…