Finally, here's an example of averse effects and their neutralization using the GPT3.5 model:

Averse Effects:

1. The GPT3.5 model demonstrates significant bias towards generating text in a particular style or on a specific topic.

2. The model may struggle with understanding context when it comes to generating more than a few tokens.

3. The model can be unreliable for factual information, mainly because it doesn't understand the context in which it is used.

Neutralization Techniques:

1. Prompt Engineering: By carefully crafting the prompts for the model, one can reduce the bias and encourage a broader range of responses.

2. Contextual Clarification: Follow up with additional contextual information to help the model understand the situation better and generate more accurate responses.

3. Factual Verification: Doublecheck facts and information provided by the model, either by crossreferencing with reliable sources or contextualizing the response in the broader framework.

4. Diverse datasets: By training on diverse datasets, the model can theoretically be made to generate a wider range of styles and topics.

5. Use Case Specificity: Various models are better suited for different types of tasks. Specific tasks might benefit from using specialized models or combining the responses from multiple models.

By applying these techniques, we can mitigate some of the averse effects of using the GPT3.5 model and enhance its performance and utility in various applications.