How Do In-Context Examples Affect Compositional Generalization?
Project Overview
This document examines the role of generative AI in education, focusing on compositional generalization and its implications for in-context learning using large language models (LLMs). It underscores the importance of carefully selecting in-context examples—considering factors such as similarity, diversity, and complexity—to enhance training outcomes. The findings indicate that effective example selection can significantly improve compositional generalization, leading to better performance in educational contexts. However, challenges persist, including difficulties with fictional words and the necessity of aligning linguistic structures. Overall, the document reveals the potential of generative AI to transform educational practices while also highlighting areas that require further research and development to maximize its effectiveness.
Key Applications
In-Context Learning with Large Language Models
Context: Educational settings where students learn about language models and compositional generalization.
Implementation: Utilized a test suite COFE to systematically investigate the effects of in-context examples on compositional generalization.
Outcomes: Demonstrated improved performance in compositional generalization with well-selected in-context examples.
Challenges: Challenges include difficulty with fictional words and the need for in-context examples to match linguistic structures.
Implementation Barriers
Technical
In-context learning struggles with fictional words and requires linguistic structures to be well-represented in examples.
Proposed Solutions: Use more natural language examples and improve model training to handle diverse linguistic structures.
Implementation
The selection of in-context examples is critical; random selection may lead to suboptimal performance.
Proposed Solutions: Develop a systematic approach for selecting in-context examples that prioritize structural similarity, diversity, and low complexity.
Project Team
Shengnan An
Researcher
Zeqi Lin
Researcher
Qiang Fu
Researcher
Bei Chen
Researcher
Nanning Zheng
Researcher
Jian-Guang Lou
Researcher
Dongmei Zhang
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Shengnan An, Zeqi Lin, Qiang Fu, Bei Chen, Nanning Zheng, Jian-Guang Lou, Dongmei Zhang
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai