Recently, while discussing the LLM technology stack with some colleagues, I found that many people confuse the concepts of ChatGPT and general LLMs. Since our team has just completed a multi-modal model training project, we conducted a technical analysis combining practical experience.
Is ChatGPT an LLM?
The answer is yes. It is a specific implementation of LLM technology. As a representative of LLM, ChatGPT not only inherits the general capabilities of LLM, but also stands out in interactive experience through dialogue optimization and human feedback reinforcement learning.
ChatGPT is built based on LLM and can be regarded as a specific application of LLM. LLM provides ChatGPT with the core capability to generate text, while ChatGPT further improves the coherence and practicality of conversations by optimizing algorithms and training data. In other words, LLM is the technical foundation, while ChatGPT is the actual product facing users.
Compared with general LLMs, ChatGPT stands out in dialogue optimization design, enabling it to maintain contextual coherence in multi-round interactions, while other models like Google Gemini focus more on multimodal processing.
LLM faces several common challenges in practical applications.
Data bias is a significant problem. Bias in training data can lead to unfair output results, especially in tasks involving sensitive domains.
High cost is one of the main obstacles to LLM deployment. Training and deployment require a lot of computing resources, which is a significant burden for small and medium-sized enterprises and research institutions.