Improving Language Model Interpretability with LMExplainer

Large Language Models (LLMs) have become a key part of understanding and using language in technology. They can perform tasks such as translating languages, generating text, and classifying information. However, these models can be hard to interpret. Their complex structure and numerous parameters make it tough for users to know how decisions are made. This can lead to concerns about their reliability, especially in important areas like healthcare and education.

#The Need for Interpretability

One major issue with language models is their lack of transparency. Users often view these models as "black boxes," meaning they can't see how input becomes output. Although techniques like attention mechanisms attempt to shed light on this, they still fall short of providing clear insights into the model’s reasoning. This is troubling, especially for people who rely on these models for critical tasks, as they need to trust the results.

The importance of making models understandable cannot be overstated. It also plays a role in fairness and safety concerns. When people understand how decisions are made, it can help build trust. This is why researchers are keen on figuring out better ways to explain the behavior of language models.

#Current Approaches to Explanation

Many methods have been explored to improve interpretability. Some focus on simpler models that can be easily understood, while others look at how to explain complex models after they have made decisions. For instance, certain methods use feature selection to show which parts of the input were most important in reaching a decision.

Some approaches attempt to provide Explanations based on attention weights, which indicate which parts of the input the model focused on. However, these explanations often fail to give a full picture. While they highlight specific inputs, they do not clarify how those inputs led to a particular outcome.

#Introducing LMExplainer

To address these issues, we propose LMExplainer. This is a new tool designed to improve the way language models explain their decisions. It does this by using a knowledge graph to help derive clear, human-friendly reasoning. By doing so, we aim to make the model's decision-making process more understandable to users.

LMExplainer is not just about providing explanations; it also looks to improve the model's performance. Through our experiments, we found that our method outperformed existing approaches on various tasks, such as question answering. We also demonstrated that LMExplainer can offer better explanations compared to previous methods, allowing users to see the rationale behind the model's predictions.

#Language Models and Their Limitations

Language models, especially pre-trained ones, have shown impressive results across various tasks. They can generate coherent narratives, translate languages naturally, and even engage in conversations. The appeal of these models lies in their capacity to understand the subtleties of human language. However, their complexity is a double-edged sword.

The intricate nature of these models makes it hard for users to understand how they work. This lack of interpretability can limit trust in the model, especially in sensitive applications. For instance, if a healthcare model suggests a treatment, doctors need to know how the model arrived at its recommendation.

#Knowledge Graphs as a Solution

One possible solution to the interpretability problem involves using knowledge graphs. These graphs represent information in a structured way and can illustrate the connections between different pieces of knowledge. By integrating knowledge graphs with language models, researchers aim to provide clearer insights into how models make decisions.

Knowledge graphs can help pinpoint specific pathways that the model used to reach its answers. However, while these methods can enhance understanding, they often present information in ways that are still challenging for people to interpret.

#Research Questions

In our work, we wanted to answer two key questions regarding language models:

1. How can we create explanations for the decision-making processes of language models that are clear and understandable?
2. How does giving explanations impact the overall performance of these models?

To explore these questions, we chose to focus on the task of question answering.

#LMExplainer's Architecture

LMExplainer operates through a series of steps. First, it extracts important elements from input data and constructs a graph representation of these elements. Next, it interprets the graph to identify which elements influenced the model’s predictions. Finally, it generates a textual explanation based on the identified reasoning elements.

This flexible approach can be applied to various language models, enabling it to work with different architectures.

#Key Element Extraction

The first step in LMExplainer is to identify key elements that influence the reasoning process. Each token in the input is treated as a content element. The model then connects these tokens to potential answers, forming a multi-relational graph. This graph incorporates external knowledge from a knowledge graph, allowing the model to analyze relationships among the elements effectively.

During the construction of this graph, we focus on retaining only the most relevant connections. This helps reduce complexity and enhances the model's ability to reason about its predictions.

#Interpreting the Graph

Once the element-graph is created, the next step is interpretation. This involves using a Graph Attention Network (GAT), which helps aggregate information from the connected nodes in the graph. Each node shares its features with its neighbors, allowing for a thorough understanding of the structure and context of the data.

Through this process, LMExplainer captures the essential features that contribute to the model's predictions. Attention weights are used to filter out less important connections, ensuring that only significant elements are retained for reasoning.

#Generating Explanations

The final step in LMExplainer is to generate explanations based on the identified key components. The goal here is to create narratives that explain why the model made specific predictions. By utilizing a template-based approach, LMExplainer can craft explanations that are straightforward and easy to follow.

The explanations are designed to highlight the model's reasoning process, making it easier for users to understand how the decisions were made. This dual-stage process first explains the chosen answer and then clarifies why other options were not selected.

#Experimental Evaluation

To assess the effectiveness of LMExplainer, we conducted experiments using two datasets: CommonsenseQA and OpenBookQA. These datasets are designed to test a model’s ability to reason with commonsense knowledge and elementary science facts.

We compared our model to various baseline approaches, including fine-tuned versions of other language models. Our results show that LMExplainer outperforms these methods in terms of accuracy on both datasets. Not only does LMExplainer enhance model performance, but it also provides clearer, more meaningful explanations for its decisions.

#Results and Findings

The performance of LMExplainer on CommonsenseQA showed a marked improvement over existing methods, with significant gains in accuracy. Similarly, on OpenBookQA, LMExplainer demonstrated competitive results. These findings suggest that incorporating explanation mechanisms could benefit the overall performance of language models.

In addition to improved performance, the quality of the explanations provided by LMExplainer was superior to those generated by other state-of-the-art models. Our method was able to generate clear narratives that effectively communicated the model’s reasoning process.

#Example Explanations

To further illustrate the effectiveness of LMExplainer, we can examine example explanations generated for different questions. The "why choose" explanations outline the key reasons supporting a given answer, while "why not choose" explanations clarify why other options were dismissed.

These explanations not only highlight the model's reasoning but also enhance the transparency of its decision-making process. Such clarity is crucial for building trust between users and the model, especially in cases where the stakes are high.

#Understanding the Impact of Different Components

To understand how various components of LMExplainer contribute to its success, we conducted ablation studies. These studies tested how different elements, such as the size of the language model and the inclusion of knowledge components, affected performance.

Our findings confirmed that larger language models led to better accuracy, while the integration of external knowledge significantly improved predictions. The interpreting component was also found to be crucial for ensuring high performance and generalizability.

#Conclusion

In summary, LMExplainer represents a significant step forward in making language models more interpretable and trustworthy. By combining the power of knowledge graphs and advanced interpretation techniques, our model not only enhances performance but also provides clear explanations of its reasoning. This work paves the way for more reliable language models that people can understand and trust, particularly in critical areas like healthcare and education.

As the field of natural language processing continues to evolve, the importance of interpretability will only grow. We hope that LMExplainer serves as a foundation for future work aimed at making language models more human-friendly and transparent.