New research from a research team at the University of Illinois at Urbana-Champaign shows that integrating code into the training data of a large language model (LLM) can significantly improve the performance and capabilities of the model. This study delves into the impact of code pre-training on LLM and analyzes the performance of LLM as an agent. Research results show that code integration can give LLM the ability to perform tasks more accurately, acquire external knowledge, and process multiple modal data. However, the research also points out the need for caution when selecting feedback signals and emphasizes the importance of enhancing code attributes in the training data to further improve the model's inference capabilities.
Research from the University of Illinois at Urbana-Champaign outlines the impact of code pre-training on LLM and traces its role as an intelligent agent. Through code integration, models can perform tasks more accurately and have the ability to acquire external knowledge and multiple modal data. However, caution is required when selecting feedback signals, as noisy cues may affect model performance on downstream tasks. In addition, the researchers believe that enhancing code attributes in the training data can directly improve the model's inference capabilities. This research provides more opportunities to further enhance model inference capabilities, but also needs to address the challenges faced when the model is connected to different functional terminals.
This research provides a valuable reference for the development of LLM, and future research will further explore how to better utilize code data while solving challenges that the model may encounter in practical applications to promote the continued progress of LLM technology and broader application.