Recently, neural networks, including ChatGPT, have become multitasking AI assistants available to any user. To get an accurate answer, it is necessary to give the neural network as much input as possible, i.e., qualitative hints or promts. The more detailed the hints, the more specific information the neural network can use. An LLM (Large Language Model) like GPT-3 is a set of huge amounts of data, and without detailed hints, a neural network may have difficulty understanding the nuances of a question and give an incorrect or irrelevant answer, i.e., start to hallucinate.
"Shot prompting." - is a technique for using cues to train AI models. Cues for AI can be of varying complexity, from simple phrases or questions to texts consisting of several paragraphs. The simpler the hint, the less effort the AI puts into it. However, "zero" cues can lead to unsatisfactory results, because in this case the AI has to make too many decisions.
"Zero shot prompting." - is an approach in which the AI uses the prompt as an autocomplete mechanism, i.e. it is given full freedom of action. However, in such a case, one should not expect a clear structured answer.
"One shot prompting." - is a technique for using hints to train AI models in which you give the AI an example of the desired result. Single hinting is used to create natural language text with a limited amount of input, such as a single example or template. This type of hint is useful if you need a specific response format.
"Few shots prompting" - is a method of training AI models in which the model is given a small number of examples, usually two to five, so that it can quickly adapt to new examples of previously seen objects. This method is used to adapt the model to new data and tasks more quickly and efficiently than training on a large number of examples.
One of the main problems with generative AI systems is hallucinations. This term is used to describe a situation where the AI produces answers that do not match reality, data, or other patterns. Typically, hallucinations occur when the AI does not have enough information to answer the question posed.
In addition, probabilistic nature generative models such as GPT can lead to hallucinations. These models use probabilistic methods to predict the next token (word or character) in a sequence given the context. Sometimes this sampling process can lead to the selection of less likely words or phrases, which can lead to unpredictable and implausible conclusions.
Lack of validation information is another cause of hallucinations. Most language models do not have the ability to fact-check their responses in real time because they do not have access to the Internet.
In addition, the complexity of models like GPT-3 can lead to hallucinations. The billions of parameters in such models allow them to capture complex patterns in the data, but it can also lead to memorization of irrelevant or false patterns, causing hallucinations in responses.
AI hallucinations can create convincing and realistic responses that can mislead people and lead to the spread of false information.
Various techniques are used to counter hallucinations, such as cue engineering, providing context and constraints, specifying the Tone of voice, and others. However, more complex tasks may require more sophisticated methods such as ToTree. In addition, training AI on a large amount of diverse data can reduce the likelihood of hallucinations.
The ToT method is an approach in which the original problem is broken down into components, which the system analyzes and expands into smaller steps or "thoughts". This makes the problem-solving process more manageable and allows the neural network to consider several different approaches to solving the problem.
Each component represents an intermediate step to solve the original complex problem. This approach allows the neural network to consider several different reasoning paths or approaches to solve the problem.
An example of using the ToT method is when several experts discuss an issue and share their thoughts in order to find the best solution. It is recommended to use English to activate the ToT method.
For example, if the question is asked, "How do I start building an artificial intelligence startup?", the system can use the ToT method to break this question down into several components such as "market research", "target audience identification", "competitor analysis", etc. Each of these components can be further broken down into smaller steps to help the system solve the problem efficiently.
The model appears to begin the reasoning process as it normally would. However, as it thinks, the model evaluates the pros and cons of each of its statements, providing additional information based on its own conclusions.
Then a second expert enters the conversation, who also builds on the previous reasoning and continues to answer the main question.
Reasoning continues until the model determines the best option for the final answer.
After the model has considered the issue from all sides and discussed each step in detail, it reaches an overall conclusion that helps to finalize the information obtained. The thought tree structure is designed to empower and address the challenges of language models by providing a more flexible and strategic approach to decision making.
Ailib neural network catalog. All information is taken from public sources.
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