In the evolving landscape of artificial intelligence, the ability to rewrite questions conversationally is becoming increasingly significant. This guide will walk you through the process of training a model tailored for conversational question rewriting, using a specific input format and providing an example from CANARD.

Understanding the Input Format

Your input data should follow this format:

Source text format: ${HISTORY} ||| ${CURRENT_QUESTION}

This requires a structured way to present the conversation history alongside the current question you want to rewrite.

Example from CANARD

Let’s consider an illustrative example:

Frank Zappa ||| Disbandment ||| What group disbanded ||| Zappa and the Mothers of Invention ||| When did they disband?

Here, the model processes the provided context and outputs the rewritten question:

When did Zappa and the Mothers of Invention disband?

How Does This Work? An Analogy

Think of training your model like teaching a robot chef how to make a recipe by following the instructions you’ve given. Just as the robot chef relies on a precise set of actions to create tasty dishes, our model needs well-structured inputs to generate coherent questions.

• The ${HISTORY} represents the recipe’s ingredients – the context of the conversation.
• The ${CURRENT_QUESTION} is like the cooking technique – it tells the robot what it’s meant to achieve.
• The output is the final dish – your goal question that’s exquisite and ready to serve!

Reproducing the Training

You can find our guide to reproduce the training process in this repo. It will give you step-by-step instructions on how to set up and fine-tune the model based on your requirements.

Troubleshooting Tips

If you encounter issues during your training process, consider the following troubleshooting ideas:

• Ensure that your input formats are strictly adhered to; any deviation may cause your model to falter.
• Double-check your training data for inconsistencies, as they can mislead the model.
• Monitor your model’s learning curves to identify if it is overfitting or underfitting.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.