CHAI for LLMs: Improving Code-Mixed Translation in Large Language Models through Reinforcement Learning with AI Feedback
Project Overview
The document presents the CHAI framework, a novel approach aimed at enhancing the performance of large language models (LLMs) in the context of code-mixed languages, which are commonly used in multilingual societies. By employing reinforcement learning from AI feedback (RLAIF), CHAI addresses the challenges LLMs encounter in accurately translating code-mixed language. The framework leverages LLMs as annotators to produce preference data for training, resulting in a significant improvement in translation quality. Experimental findings reveal that models powered by the CHAI framework outperform existing state-of-the-art LLMs in tasks involving code-mixed translation, showcasing the potential of generative AI in educational settings where multilingual communication is essential. Overall, the CHAI framework exemplifies how generative AI can effectively enhance language translation capabilities in education, facilitating better understanding and communication among diverse linguistic groups.
Key Applications
CHAI (Code Mixed Understanding via Hybrid AIInstruction)
Context: Machine translation tasks for code-mixed languages, specifically targeting users who speak languages like Hindi and English.
Implementation: Utilizes reinforcement learning from AI feedback to improve code-mixed language translation capabilities.
Outcomes: CHAI-powered models achieved a 25.66% improvement in win rate over existing models in code-mixed translation tasks.
Challenges: Existing LLMs struggle with the grammatical and syntactical variations of code-mixed languages, and collecting sufficient training data can be time-consuming.
Implementation Barriers
Technical Barrier
Current LLMs have been trained predominantly on monolingual data, leading to poor performance on code-mixed tasks.
Proposed Solutions: CHAI proposes using LLMs as annotators to generate preference data for training, thereby addressing the data scarcity issue.
Socio-Economic Barrier
Relying on AI feedback may reduce opportunities for human annotators, especially in regions where labor is economically vital. This raises ethical implications regarding the replacement of human feedback with AI.
Proposed Solutions: The document suggests a need for careful consideration of the ethical implications of replacing human feedback with AI.
Project Team
Wenbo Zhang
Researcher
Aditya Majumdar
Researcher
Amulya Yadav
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Wenbo Zhang, Aditya Majumdar, Amulya Yadav
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