In the previous lecture, we discussed in detail the evolutionary history of language models. The main reason why chatbots have become so popular today is not because they can chat with people or help them with their homework. In fact, chatbots are not good at doing homework, although they can solve some problems in middle schools and universities. The answers they provide are often just words. The truly remarkable thing about chatbots is that if they continue to develop and expand their application area, they may be able to solve many problems that originally required humans to solve.
Now, let’s explore the capabilities and limitations of language models. The foundation of chatbots is the language model, so their limits are also limited by the limits of the language model. We can categorize the things that language models can do into three categories.
The first category is information form conversion. This includes tasks such as automatic speech recognition and machine translation. In automatic speech recognition, the input information is voice sound waves, and the output information is text. Machine translation converts text from one language to another. However, it’s important to note that information conversion usually loses some information. For example, the cultural nuances in language are often lost in machine translation.
The second category is text generation on request. This is the main task of chatbots, such as answering questions, replying to emails, and writing simple paragraphs. In this type of work, the amount of information input is significantly less than the amount of information output. This creates uncertainty and requires additional information. The source of this supplementary information is the language model itself. If the language model contains a lot of relevant information about a topic, it can produce high-quality text. Otherwise, the answers or content it generates may be irrelevant.
The third category is information simplification. This involves reducing more information to less, such as writing summaries, performing data analysis, and analyzing financial statements. When simplifying information, there is a choice of what information to keep and what to delete. Different individuals may have different opinions on what is important. Computers excel in this type of work because they can process data quickly and objectively. However, they lack personalization.
While language models like ChatGPT have made significant progress in these areas, there are still limitations. They cannot create answers or content on their own. They rely on pre-existing knowledge and information contained in the language model. They also lack the ability to personalize their output. However, with continued advancements, language models are expected to become even more capable in the future.
In conclusion, language models have the potential to revolutionize various tasks, from information conversion to text generation and information simplification. By understanding their capabilities and limitations, we can leverage their strengths and complement them with human expertise. As language models continue to evolve, they will play an increasingly important role in our lives.
