Zero-Shot End-to-End Relation Extraction in Chinese: A Comparative Study of Gemini, LLaMA and ChatGPT
This study addresses the problem of zero-shot end-to-end relation extraction in Chinese for researchers and developers working on natural language processing tasks, providing an incremental step towards improving the adaptability of large language models to complex linguistic tasks.
This study compared the performance of ChatGPT, Gemini, and LLaMA on zero-shot end-to-end relation extraction in Chinese, with ChatGPT demonstrating the highest overall performance and Gemini achieving the fastest inference speed. The results show that while LLMs are promising for relation extraction, there is still a need for further adaptation to improve accuracy and efficiency.
This study investigates the performance of various large language models (LLMs) on zero-shot end-to-end relation extraction (RE) in Chinese, a task that integrates entity recognition and relation extraction without requiring annotated data. While LLMs show promise for RE, most prior work focuses on English or assumes pre-annotated entities, leaving their effectiveness in Chinese RE largely unexplored. To bridge this gap, we evaluate ChatGPT, Gemini, and LLaMA based on accuracy, efficiency, and adaptability. ChatGPT demonstrates the highest overall performance, balancing precision and recall, while Gemini achieves the fastest inference speed, making it suitable for real-time applications. LLaMA underperforms in both accuracy and latency, highlighting the need for further adaptation. Our findings provide insights into the strengths and limitations of LLMs for zero-shot Chinese RE, shedding light on trade-offs between accuracy and efficiency. This study serves as a foundation for future research aimed at improving LLM adaptability to complex linguistic tasks in Chinese NLP.