Search Results for author:
Found 4 papers, 2 papers with code
Post-Semantic-Thinking: A Robust Strategy to Distill Reasoning Capacity from Large Language Models
Chain of thought finetuning aims to endow small student models with reasoning capacity to improve their performance towards a specific task by allowing them to imitate the reasoning procedure of large language models (LLMs) beyond simply predicting the answer to the question.
Multi-Feature Integration for Perception-Dependent Examination-Bias Estimation
Eliminating examination bias accurately is pivotal to apply click-through data to train an unbiased ranking model.
Pretraining De-Biased Language Model with Large-scale Click Logs for Document Ranking
Pre-trained language models have achieved great success in various large-scale information retrieval tasks.
HCNet: Hierarchical Context Network for Semantic Segmentation
In order to solve the above problem, we propose a hierarchical context network to differentially model homogeneous pixels with strong correlations and heterogeneous pixels with weak correlations.
