You’ve seen the benchmarks, some GenAI models rank higher than others in reasoning, language understanding, or coding. Why is this the case?
Yes, the training phase of a GenAI model shapes its ability to effectively handle certain use cases. That’s why GenAI model developers are investing heavily in research to optimize model training.
While some developers may reveal their findings, many prefer to guard their GenAI model training secrets. Well, today is your lucky day!
Behind every well-trained GenAI model are three key ingredients. That’s the data, algorithm, and the feedback loop.
While the algorithm defines how the model learns from data, feedback from automated evaluation systems or humans is also crucial to fine-tune the model’s performance.
Here are smart GenAI training moves to create powerful, effective models:
Some say that GenAI models need more data to perform better. However, data quality is what really matters.
Before feeding a model data, you must clean it. Based on the purpose or use case of the model, filter out low-quality or irrelevant data. Also, remove conflicting samples, normalize formats, and get rid of duplicates.
Note that the processes of data cleaning varies based on the type of GenAI training data. Most teams use automated scripts to handle data cleaning. Nonetheless, ensure you have human oversight in place to spot subtle data errors.
Apart from cleaning data, curate diversity. Do this by building a dataset composed of a wide range of scenarios relevant to a model’s use case.
And, while at it, ensure the scenarios fit the environment the model is meant to operate in. This is how you end up with a model that not only learns facts but also ‘understands’ the tone, style, and behavior to adopt in its response.
Rather than giving a whole training dataset to a GenAI model, developers structure or divide the data into learning difficulty levels. Then, they gradually feed the data to the model, moving from easy to complex examples as the model improves.
This model teaching technique mimics how you’d teach a child. The model first learns from the easy examples, grasping basic patterns. For instance, when training a language model, easy data includes short, precise sentences and simple word meanings.
The model’s performance determines how quickly you expose it to advanced or complex examples. Training a model this way builds a strong foundation, increasing the likelihood of it performing better when exposed to unseen tasks because it has learned to reason step-by-step.
Training a model in stages also reduces instances of bias and errors. How? Before moving to the next level of complexity, you must test the model’s accuracy and stability.
Even after effectively training a GenAI model, it doesn’t necessarily know what people like. It only extracts ‘understanding’ from the provided data, leaving room for the possibility of it being unclear, off-topic or even rude. So, how do we solve this problem?
Reinforcement Learning from Human Feedback (RLHF) is the solution. RLHF is the process of teaching a model how to behave by rewarding it for good answers and discouraging bad answers. The whole process relies on human judgement, resulting in a model that provides helpful, safe, and natural answers.
To put RLHF into action, you need to have a reward system in place. Then, allow the AI model to reference human input after each response and adjust its internal patterns to maximize “rewards” or good feedback.
Human feedback helps a GenAI model know what people prefer. It also helps the model to prioritize safety, empathy, clarity, and truth in its responses. Some model development teams even infuse updated human data to enhance the model’s output.
Most publicly available GenAI models like ChatGPT are trained on vast datasets, allowing everyday users to use them for a wide range of tasks. Nonetheless, you can fine-tune open-source models with domain expertise.
Supplying a pre-trained open-source model with expert knowledge in a specific field like law or finance helps it think and speak like an expert in that field.
Before giving a model specialized data, ensure the training dataset is curated, verified, and context-rich. This helps the model learn the correct answers and the thought processes in the select field. The model reasons, explains, and makes decisions just like an expert in the field does.
Even though you can use a general-purpose model for domain-specific tasks, the model is more likely to hallucinate at some point. This is because it is trained to know a little about everything. So, to reduce the possibility of it guessing a lot, you provide it with domain-specific data to make it more accurate and cautious.
For instance, if you are into photography, you want to go for a model curated for professional background removal or photo editing. A general-purpose model may do the job but with more cutout or editing errors.
Besides fine-tuning a GenAI model with domain expertise, you can also fine-tune how it understands and follows human commands.
As part of the training process, show the model examples in which a human gives an instruction and the correct response is provided. With time, the model learns patterns defining what a good response looks like. This is the reason advanced models seem to “get” what the user wants even when they phrase instructions casually.
Moreover, you can help the AI model understand how to manage back-and-forth conversations. This is what we call dialogue optimization.
To teach a model to engage in a conversational flow that feels context-aware and natural, you must interact with it. As it responds, you help it fix unclear responses and rephrase awkward ones while rating the model’s dialogue capabilities. Rating assists the model with deciding what weights to adjust, refining its conversational instincts.
Once trained, GenAI models can generate articles, emails, reports, and summaries in seconds. Change the tone and style and the model can still handle the task. The same goes for code generation and debugging, image classification or generation, and video generation. However, there’s a catch!
For a GenAI model to be great at what it does, it must undergo effective training. That’s why we’ve covered the top secrets around GenAI model training. As you put some into action, keep researching to discover even more.
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