ChatGPT - It is not the holy grail

Any new technology attracts much attention, and ChatGPT is at the forefront of a significant leap in human interaction with a machine we have ever experienced. Understanding the limitations of ChatGPT and all the beautiful things it can accomplish is essential. I have illustrated a few ways to overcome these limitations as we advance.

Lack of context:?Since language models such as ChatGPT do not have access to external information, they may not be able to deliver accurate or relevant responses to questions that need knowledge about the world beyond the training data. This means that the language model may not offer a correct or appropriate answer if you ask a question that requires knowledge of the world beyond the training data.

For instance, if you ask ChatGPT about current events or news, it cannot provide information about events after the model's knowledge cutoff (in this case, 2021). Similarly, suppose you ask a question about a topic that was not included in the training data. In that case, the model may not have any relevant information and may be unable to deliver a suitable response.

This absence of context might be a shortcoming of language models such as ChatGPT. It is important to remember this when engaging with the model.

Bias in the training data: Language models are only as good as the data on which they are trained; if the training data contains biased or discriminatory language, the language model may perpetuate these biases.

Several methods exist for identifying and reducing bias in big language models, but it is vital to keep the following things in mind:

• Evaluate the training data:?Reviewing the training data to identify any possible biases thoroughly is essential. This may involve searching for trends in the language or substance of the data and studying the demographics of the individuals represented by the data.
• Use varied training data: Using a diverse and representative training dataset can assist in reducing model bias. This may require gathering information from several sources, including information from underrepresented populations.
• Utilize bias-mitigation techniques: Numerous strategies can minimize bias in language models, including counterfactual data augmentation, adversarial training, and pre-processing methods such as debiasing word embeddings.
• Monitor and test the model: Regular monitoring and testing of the model for bias can assist in identifying and resolving any potential problems. This may involve analyzing the model's outputs for biased language or testing the model's fairness using human testers.

Limited understanding of language:?While language models can generate human-like text, they have a different level of language comprehension than humans and may not fully comprehend the meaning or context of specific phrases or concepts.

There are multiple techniques to increase the language comprehension of large language models such as ChatGPT:

• Utilize a more extensive and diverse training dataset. A larger and more diversified training dataset can offer the model additional instances and a broader range of language and context, enhancing its language comprehension.
• Fine-tune the model: When the model is fine-tuned for a specific job or domain, it can better comprehend the language and context of that domain. For instance, by fine-tuning ChatGPT on a dataset of legal papers, its comprehension of legal terminology and concepts may be enhanced.
• Transfer learning uses a pre-trained language model as a starting point for a new activity. It can be an effective method for enhancing language comprehension for that work.

Overreliance on the model:?It is important to remember that language models are not a replacement for human expertise and judgment and should not be relied upon solely for decision-making or problem-solving tasks.