We live in an era overflowing with new information and knowledge. Among them, the ability to find and utilize information that we’re curious about or need is increasingly establishing itself as a crucial competitive advantage.
Until recently, we relied on web searches, but now, with the emergence of chatbots based on large language models like ChatGPT (LLM-chatbots), the way we seek information is changing. This is because you can now directly ask LLM chatbots in natural language if you have a question.
Until now, if someone had a query, they would define search terms that might contain the answer and then search on platforms like Google, Bing, and Naver. They'd read the resulting links to find their desired answer. Given the vast amount of diverse information on the web, the likelihood of finding what one is curious about is high.
However, web search has disadvantages, as shown in Table 1. The first is that to quickly find the desired information, one must craft effective search terms. Second, because there's a plethora of information on the web, choosing the most suitable document from numerous search results isn't straightforward. Lastly, web searches provide documents with high relevance but don't necessarily present the desired answer. To obtain the desired answer, one has to invest time and effort in reading and deciphering the provided documents.
Table 1. Disadvantages of Searching
| Disadvantage | Description |
|---|---|
| Keyword Generation | To find the desired information, it's crucial to craft the right search term. |
| Selecting Search Results | There are numerous similar matches in the search results, and the right one must be identified among them. |
| Crafting an Answer | After reviewing the search results, one must derive the answer they seek. |
<aside> <img src="/icons/first-aid_pink.svg" alt="/icons/first-aid_pink.svg" width="40px" /> With LLM-Chatbot, one can directly ask in natural language what they're curious about. Since it provides answers after reading multiple documents, it's more convenient than searching.
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On the other hand, it's important to consider that the LLM-Chatbot might have difficulty or even be unable to provide answers to the latest information or content it hasn't been trained on. Additionally, since LLM-Chatbot is based on neural network-based learning, the abstracted information may differ in detail during the conversion to an answer. This means it may sometimes provide incorrect or strange answers due to abstraction errors.
Table 2. Disadvantages of LLM-Chatbot
| Disadvantage | Description |
|---|---|
| Dependency on Trained Data | It's hard to answer about the latest information or content that hasn't been included in its training. |
| Characteristics of Abstracted Information | Due to the abstraction process in the neural network training, the response might be incorrect or seem bizarre. |
To overcome the disadvantages of the LLM-Chatbot, one can use web search-based chatbots (WebChatbots) like BingChat. This method extracts keywords from the user's question, searches the web for relevant content, and then converts the content to a response using LLM. This allows the chatbot to provide answers even to the latest information not covered by LLM.
WebChatbots combine web search capabilities with LLM-Chatbot, making them useful in various search situations. However, since the chatbot hasn't been trained on the web-searched information, there's a possibility it might not fully understand and might generate an answer based on abstract knowledge. For general information queries, it might be better to use the LLM Chatbot.