Technical Limitations of ChatGPT

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Technical Limitations of ChatGPT

Sometimes ChatGPT provides responses that are accurate but are really erroneous or illogical. Fixing this problem is difficult because: (1) there is currently no source of truth during RL training; (2) making the model more cautious makes it decline questions that it can answer correctly; and (3) supervised training deceives the model because the best response depends on the model’s knowledge rather than the demonstrator’s knowledge.

The input phrase can be changed, and ChatGPT is sensitive to repeated attempts at the same question. For instance, the model could claim to not know the answer if the question is phrased one way, but with a simple rewording, they might be able to respond accurately.

The model repeatedly states that it is a language model developed by OpenAI and utilizes other overused words. These problems are caused by biases in the training data (trainers favor lengthier responses that appear more thorough) and well-known over-optimization problems.

When the user provides an uncertain query, the model should ideally offer clarifying questions. Instead, our present models typically make assumptions about what the user meant.

Although we’ve worked to make the model reject unsuitable requests, there are still moments when it’ll take negative instructions or behave inimically. Although we anticipate some false negatives and positives for the time being, we are leveraging the Moderation API to alert users or prohibit specific categories of hazardous material. In order to help us in our continued efforts to enhance this system, we are glad to gather user input.