Dongping Chen

Dongping Chen, Jiawen Shi, Yao Wan et al.

While Large Language Models (LLMs) have achieved remarkable success across various applications, they also raise concerns regarding self-cognition. In this paper, we perform a pioneering study to explore self-cognition in LLMs. Specifically, we first construct a pool of self-cognition instruction prompts to evaluate where an LLM exhibits self-cognition and four well-designed principles to quantify LLMs' self-cognition. Our study reveals that 4 of the 48 models on Chatbot Arena--specifically Command R, Claude3-Opus, Llama-3-70b-Instruct, and Reka-core--demonstrate some level of detectable self-cognition. We observe a positive correlation between model size, training data quality, and self-cognition level. Additionally, we also explore the utility and trustworthiness of LLM in the self-cognition state, revealing that the self-cognition state enhances some specific tasks such as creative writing and exaggeration. We believe that our work can serve as an inspiration for further research to study the self-cognition in LLMs.

Haiyang Tang, Zhenyi Liu, Dongping Chen et al.

Recent advancements in large language models (LLMs) have notably propelled natural language processing (NLP) capabilities, demonstrating significant potential in safety engineering applications. Despite these advancements, LLMs face constraints in processing specialized tasks, attributed to factors such as corpus size, input processing limitations, and privacy concerns. Obtaining useful information from reliable sources in a limited time is crucial for LLM. Addressing this, our study introduces an LLM-based Q&A system for safety engineering, enhancing the comprehension and response accuracy of the model. We employed prompt engineering to incorporate external knowledge databases, thus enriching the LLM with up-to-date and reliable information. The system analyzes historical incident reports through statistical methods, utilizes vector embedding to construct a vector database, and offers an efficient similarity-based search functionality. Our findings indicate that the integration of external knowledge significantly augments the capabilities of LLM for in-depth problem analysis and autonomous task assignment. It effectively summarizes accident reports and provides pertinent recommendations. This integration approach not only expands LLM applications in safety engineering but also sets a precedent for future developments towards automation and intelligent systems.