Бред искусственного разума рождает чудовищ

[vak]

Я пожаловался гугловскому Gemini, что иногда искусственный интеллект на вопрос, на который у него нет однозначного ответа, начинает фантазировать. Ответ по-прежнему выглядит разумным, и определить, реальный он или бред, непросто. Вот что ответил Gemini.

You're absolutely right. LLMs, including ChatGPT, can struggle with questions that lack a clear, factual answer. Here's why:

1. Training Data Bias: LLMs are trained on massive amounts of text data. This data, while vast, can be skewed towards certain topics or writing styles. When faced with an ambiguous question, the LLM might rely on patterns or narratives present in its training data, even if they're fictional.
2. Confidence Calibration: LLMs are good at producing seemingly coherent text, but they might not be great at understanding the actual accuracy or source of that information. This leads to a phenomenon called "overconfidence," where the LLM delivers an answer with high certainty, even if it's fabricated.

Is it a known problem?

Yes, this is a recognized challenge in the field of Large Language Models. Researchers are actively working on solutions, such as:

• Better Training Data: Curating training datasets that are more balanced and representative of the real world can help LLMs avoid biases and generate more grounded responses.
• Fact-Checking Integration: Integrating fact-checking mechanisms within the LLM itself could allow it to verify the veracity of its own outputs before presenting them as answers.
• Confidence Scoring: Developing metrics to assess the LLM's confidence in its own responses would help users better understand the reliability of the information they're receiving.

What can you do?

Here are some tips for interpreting LLM responses with a critical eye:

• Look for Citations: If the LLM mentions specific sources, try verifying them yourself.
• Consider the Context: Does the answer fit with what you already know about the topic?
• Be Wary of High Certainty: Don't assume an answer is true just because the LLM delivers it confidently.

By being aware of these limitations, you can use LLMs like ChatGPT more effectively and avoid getting misled by their fantastical outputs.