Enhancing Language Models with Attention Based Credit

Reinforcement Learning From Human Feedback (RLHF) has changed how we train large language Models to follow instructions. Traditionally, these models generate responses to given inputs, and then a separate system gives a score to these responses. This method can be tricky because a language model has to choose many words one by one but only gets one score at the end, which isn’t very helpful for learning.

This paper introduces a new method called Attention Based Credit (ABC). The goal of ABC is to provide more useful feedback by using information from the model's attention system. This makes it easier for the model to learn because rewards are given at the word level instead of just at the end of the response. We show that this new method does not complicate the existing learning process and can lead to faster and better results.

#The Challenge of Sparse Rewards

In standard RLHF, when a model completes a task, the feedback can often be very sparse. This means the model only gets a score at the end, without knowing which specific actions during the task were good or bad. This setup can confuse the model and make it hard for it to learn effectively.

For example, if a model generates a long text, it might choose many words that lead to a final score, but it won’t know which words were helpful or harmful. This can lead to issues like vanishing gradients, where the model struggles to improve because it doesn’t receive detailed feedback. In some cases, researchers have tried to make training more stable by using different techniques, but those can be complex and may not fully address the issue.

#Introducing Attention Based Credit (ABC)

ABC aims to solve the problem of sparse feedback by utilizing attention weights from the model. Attention weights help the model understand which words are more important for its predictions. By thinking of the attention map as a tool for credit assignment, we can redistribute the rewards across the entire text, not just at the end.

This means when the model gets a final score, that score can be shared among the individual words based on how much attention they received during the generation process. Essentially, we are giving each word a bit of the reward according to its relevance in forming a good response.

The main benefits of ABC are:

1. Faster Learning: By providing feedback at the word level, the model can learn quickly and adjust its behavior based on detailed feedback.

2. Improved Stability: As more rewards are given throughout the response, the training becomes more robust and less likely to fail.

3. No Extra Cost: The method uses information already available in the model, so it doesn’t require significant additional computation.

#How ABC Works

To explain how ABC operates, we need to consider how rewards are normally structured. Traditionally, once a response is fully generated, the model receives a score based on how good that response is. With ABC, we take a look at the attention weights of each word to see which ones mattered most to the final score.

Imagine a language model that generates the sentence "The quick brown fox jumps over the lazy dog." When it generates this sentence, the model will pay more attention to some words like "jumps" and "fox," because they are crucial for the meaning of the sentence. By using the attention weights, we can give more of the final reward to those important words rather than distributing it equally across the entire sentence.

#Why This Matters

Using ABC, we can simplify the learning process for language models. When these models receive more granular, meaningful feedback, they can adapt their predictions more effectively. This is particularly important in tasks that require accuracy, such as customer service or technical support, where the quality of responses can greatly impact user satisfaction.

Also, as we train models to be useful assistants, their ability to generate helpful and relevant responses will improve. Essentially, ABC allows models to better align with human preferences by giving feedback that matches how humans would evaluate responses.

#Experimental Results

To see how well ABC works, experiments were conducted using three different tasks. The tasks varied in their complexity and requirements:

1. Positive Generation: Here, models were trained to create movie reviews with a positive tone. We used a smaller model called GPT2. This test was helpful for understanding how ABC could help the model generate responses consistently.

2. Summarization: In this task, models needed to summarize Reddit posts. This involved using a larger model called GPT-J and tested how well ABC could help create concise summaries based on user preferences.

3. Single-turn Dialogue: This task involved training a model for dialogue systems, helping it generate responses to questions posed by users. The goal was to ensure that the model could engage in a way that felt natural and helpful.

Across these experiments, the results showed that models using ABC reached optimal performance much faster than those using traditional methods. Models trained with ABC were able to produce responses that were not just good but also more consistent in quality.

#Advantages of ABC

The advantages of using Attention Based Credit can be summarized as follows:

1. Efficiency in Learning: ABC reduces the number of training steps needed for models to reach their peak performance. This leads to faster deployments and improvements in model accuracy.

2. Consistency: With denser rewards, the model benefits from a more reliable feedback loop, allowing it to maintain high performance over different tasks.

3. Improved User Experience: As models become better at generating helpful responses, the overall user experience enhances. This is particularly relevant in applications like chatbots and virtual assistants, where responses need to be timely and appropriate.

#Conclusion

As language models are increasingly used for various tasks, the importance of effective training methods becomes clear. The introduction of Attention Based Credit offers a simple yet powerful solution to enhance the learning process. By providing more detailed feedback through attention weights, we can help these models generate better responses while also making the training process faster and more stable.

In moving forward, it will be essential to continue exploring ways to extract more information from existing models. Techniques like ABC provide a strong foundation for future innovations in training language models to align more closely with human expectations and preferences, ultimately leading to safer and more effective AI systems.

The findings of this paper highlight the significance of dense rewards in reinforcement learning and the impact that subtle changes in feedback mechanisms can have on the overall performance of language models.