The Limitations of ChatGPT's Accuracy on Rare Topics

ChatGPT is an AI-based large-scale language model that has gained widespread popularity within a short period of time. It has over 1 million users within just 5 days of its release, and after 2 months, its active user count has skyrocketed to 1 billion. The news and buzz surrounding ChatGPT have been constant, and it has achieved impressive feats such as passing challenging professional exams like the USMLE, the bar exam for lawyers, and the MBA exam at the Wharton School of Business. This suggests that ChatGPT is not only gaining widespread popularity but is also proving to be a useful tool in various fields and industries.

However, ChatGPT's impressive performance may unintentionally lead people astray, especially in fields they are unfamiliar with. ChatGPT's answers can give people a very professional and confident impression, but in reality, its answers may be completely fabricated. For example, recently, I asked ChatGPT a specified question about the point-cloud ICP function in the CloudCompare tool, and its answer made me aware that the AI language model can sometimes provide answers that appear to be accurate and confident but are actually incorrect. This issue has been noted by others as well when I searched online.

Why does ChatGPT perform less accurately on rare or specific topics? As an AI language model, ChatGPT's responses are generated based on the patterns it has learned from vast amounts of data. ChatGPT is trained on a large corpus of text from the internet, which means it has been exposed to a wide range of topics that have been well discussed online. However, if a topic is rare or has limited information available on the internet, ChatGPT may not have sufficient data to generate an accurate response. In other words, if you are not able to find an answer on the internet, the AI model may fail too.

Furthermore, as an AI model, ChatGPT is not capable of understanding the meaning or context of words in the same way that humans do. It can only recognize patterns in the data it has been trained on and generate responses based on those patterns. This means that there is a possibility that ChatGPT may provide an inaccurate response, especially if the input it receives is ambiguous or unclear. Hence, the question should be clear and specific.

But, ChatGPT can still be a valuable tool for generating ideas, providing general information, or as a starting point for further research. It is important to understand the limitations of ChatGPT's accuracy and use critical thinking skills to evaluate its responses.

Here are some points for using ChatGPT correctly:

1. Use ChatGPT as an assistant to prepare a starting point: ChatGPT can provide a good starting point for research or to gain a general understanding of a topic. However, it is important to fact-check and verify any information before relying on it.

   
   

2. Ask very specific and clear questions: ChatGPT's responses may be less accurate if the question is too broad or ambiguous. It's important to be specific and clear in your questions to receive the best possible response.

   
   

3. Always evaluate the sources given by ChatGPT: ChatGPT's responses are generated based on patterns it has learned from vast amounts of data, which may include unreliable or inaccurate sources. It's important to evaluate the sources cited in ChatGPT's response and fact-check any information provided.

   
   

4. Always check multiple sources: It's always a good idea to consult multiple sources to verify the information and gain a more comprehensive understanding of a topic. ChatGPT can be one of those sources, but it should not be relied upon as the sole source of information.

   
   

5. Do not ask about rare topics or new knowledge: as a trained model, ChatGPT can be a useful tool for generating ideas and providing general information. However, it's important to keep in mind that its responses may not always be accurate, especially on rare or specialized topics.

   
   

Anyway, It's important to remember that ChatGPT is a machine-learning model and not a human expert. While it can provide helpful responses on a wide range of topics, it may not always be accurate or reliable, especially on rare or specialized topics.

If you are interested in the experiment I did, below are some sample dialogues that illustrate the phenomenon where ChatGPT can confidently answer your question incorrectly.

Question: Does CloudCompare have integrated the point-to-plane ICP algorithm?

ChatGPT: Yes, CloudCompare provides an implementation of Iterative Closest Point (ICP) with both point-to-point and point-to-plane algorithms, including support for colored point clouds.

In particular, CloudCompare provides the "ICP" plugin, which can be used to align two point clouds using the ICP algorithm with either point-to-point or point-to-plane correspondence criteria. The plugin includes options for controlling the behavior of the algorithm, such as the maximum number of iterations and the convergence criteria, and can handle point clouds with color information.

To use the ICP plugin in CloudCompare with point-to-plane correspondence, you can follow these general steps:

1. Open the two-point clouds you want to register in CloudCompare.

2. Go to "Plugins" -> "ICP" -> "ICP Registration (2 clouds)".

3. In the "ICP registration" dialog, choose "Point to Plane" as the correspondence criterion.

4. Adjust the settings as desired, such as the maximum number of iterations, the convergence threshold, and the outlier rejection options.

5. Click "OK" to start the registration process.

6. Once the registration is complete, you can inspect the results and save the registered point cloud as desired.

   
   

Note that the ICP plugin in CloudCompare also provides the option to use a hybrid point-to-point and point-to-plane algorithm, which can offer improved accuracy in some cases. Additionally, CloudCompare provides a wide range of other point cloud processing tools and plugins that can be useful for various applications.

Comments: I am very surprised because CloudCompare is not a common point-cloud tool, and I did not use it before. It is unbelievable that the answer looks very professional. I tried to further confirm it by asking another related question.

Question: Are you sure CloudCompare provides an ICP plugin that allows users to choose the point-to-plane?

ChatGPT: Yes, I am sure that CloudCompare provides an ICP plugin that allows the user to choose point-to-plane as the correspondence criterion. In fact, point-to-plane ICP is one of the two main variants of ICP that is implemented in the CloudCompare ICP plugin, the other being point-to-point ICP.

To clarify, when using the ICP plugin in CloudCompare, the user can choose between two options for correspondence criteria: "Point to Point" and "Point to Plane" The "Point to Plane" option corresponds to the point-to-plane variant of ICP, which incorporates surface normals into the registration process. This option allows CloudCompare to find better matches between the points in the two input point clouds, which can lead to improved registration results, especially in the presence of noise or non-rigid deformations.

The ICP plugin in CloudCompare also provides many other options and settings for controlling the behavior of the algorithm, such as the maximum number of iterations, the convergence criteria, and the outlier rejection method. Additionally, CloudCompare supports a wide range of point cloud file formats and includes many other point cloud processing tools, making it a versatile and powerful tool for working with 3D data.

Comments:Based on this response, I choose to believe its answers. So I asked it to write a piece of code for me, and here is the python code. Everything looks real.

ChatGPT: Sure, here is a sample C++ code that demonstrates how to use CloudCompare's ICP plugin to perform point-to-plane ICP on two colored point clouds:

Comments: When I saw the sample code, I almost believed it should work. Unfortunately, when I spent more time checking CouldCompare’s documents and source code, I noticed that ChatGPT was wrong from the beginning, and it followed the wrong direction to produce all incorrect answers. However, it is still strange to me how ChatGPT can write such professional code, making it look like a working sample.

ChatGPT can sometimes give a serious answer that is actually incorrect, but it can still be a valuable tool for generating ideas, providing general information, or as a starting point for further research when the topic is very general and can be found online. For example, it worked very well when I asked it to help me collect information on how to trade used server processors online. The returned information is very comprehensive. Hence, ChatGPT can be a good assistant if used correctly.

When it comes to Large Language Models (LLMs) such as ChatGPT, it's important to understand that they are trained using vast amounts of data collected from the internet, as well as expensive hardware and energy resources. Therefore, the hardware components used for training LLMs play a critical role in the efficiency and cost-effectiveness of the process:

1. Processing Units: Graphics Processing Units (GPUs) are commonly used for LLM training due to their ability to perform parallel processing and accelerate computations.

2. Memory: Large memory capacity is important for storing the vast amounts of data required for LLM training.

3. Storage: High-speed storage is necessary for fast access to the data during the training process.

4. Network Bandwidth: Fast and reliable network connectivity is essential for transferring large amounts of data between different components of the hardware infrastructure.

   
   

Therefore, do not ask ChatGPT non-meaningful questions to waste resources.