Evaluating RLHF models

1. Evaluating RLHF models

Now that we've explored all stages of the RLHF process, we will go over concepts for model evaluation.

2. Automation metrics

There are many ways to evaluate a model, and we will talk about three of them here. First are automated metrics. These are measurements calculated using algorithms or formulas and need a reference (called ground truth). Examples include using accuracy and F1 score for classification tasks,

3. Automation metrics

and ROUGE and BLEU for text generation and summarization. These metrics check how similar a model's output is to human-generated text,

4. Automation metrics

by measuring how many words overlap over the total words. In the two statements presented here for example, the overlapping words are in bold and the ROUGE-1 score is 0.83, as 5 words out of 6 overlap.

5. Artifact curves

The second method is to log our metrics and learning curves during training. We can use a library such as Weights & Biases, or wandb, for this. By including 'log_with' equal to 'wandb' in our configuration and initializing wandb with wandb dot init, all our training metrics, such as loss and performance curves, will be automatically logged and visualized on the wandb platform. This makes it easy to monitor our model's performance.

6. Artifact curves

During training, the reward should increase as the model learns. A steady upward trend in the reward indicates that the model is improving and producing more desirable outputs based on the feedback it's receiving. However, if the reward plateaus or fluctuates wildly, it might suggest that the model is struggling to learn or that our reward function needs adjustment. Another curve to check is the KL loss. This should remain stable or increase gradually. If it is too low, it might mean the model isn't learning enough from the human feedback and is sticking too closely to the pre-trained model's behavior. If it becomes too high, the model may be diverging too much from the pre-trained behavior.

7. Human centered evaluation

The third method is to use human reviewers or other models to rate the model's output. Researchers have found that learning curves and human/model evaluations are the most useful. Human labelers can provide nuanced feedback on aspects such as coherence, relevance, and overall quality. This method is particularly valuable for tasks requiring subjective judgments or a deep understanding of context. On the other hand, using another model for evaluation can offer scalability and consistency, as it can process large volumes of data and provide objective metrics quickly. Finally, combining both approaches can give a comprehensive view of the model's performance.

8. Let's practice!

Now that we understand how to evaluate model performance, let's put our knowledge into practice.

Create Your Free Account

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.