Search Results for author:
Found 4 papers, 0 papers with code
A Divide-And-Conquer Approach for Multi-label Multi-hop Relation Detection in Knowledge Base Question Answering
However, most of existing approaches only detect one single path to obtain the answer without considering other correct paths, which might affect the final performance.
Complicate Then Simplify: A Novel Way to Explore Pre-trained Models for Text Classification
However such way might not fully explore the knowledge in PTMs as it is constrained by the difficulty of the task.
Addressing Order Sensitivity of In-Context Demonstration Examples in Causal Language Models
This approach utilizes contrastive learning to align representations of in-context examples across different positions and introduces a consistency loss to ensure similar representations for inputs with different permutations.
The Mystery of In-Context Learning: A Comprehensive Survey on Interpretation and Analysis
Understanding in-context learning (ICL) capability that enables large language models (LLMs) to excel in proficiency through demonstration examples is of utmost importance.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
