MExGen: A New Way to Explain Language Models

In recent years, language models have become important tools for generating text. These models can summarize long documents, answer questions, and create human-like responses. However, understanding how these models make decisions is challenging. This article will discuss a new framework for explaining how generative language models work, helping users to see how input text influences the generated output.

#The Need for Explanations

As language models are used in more applications, it becomes crucial to explain their outputs. When a model generates a summary or answers a question, it is essential to understand what parts of the input text were most meaningful in producing that output. This understanding can improve trust in these models, benefiting users and developers alike.

#Current Explanation Methods

There are existing methods that provide explanations for models, particularly in text classification tasks. Two popular methods are LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations). These techniques give scores to different parts of the input, showing how much each part contributes to the model's decision.

However, these methods have limitations when applied to generative models. Generative models produce text rather than numerical values, making it difficult to apply standard explanation techniques. Additionally, generative tasks often involve longer inputs, which complicates the explanation process.

#Introducing MExGen

To tackle these challenges, a new framework called MExGen (Multi-level Explanations for Generative Language Models) was developed. MExGen adapts existing attribution algorithms to better explain generative tasks. It uses various techniques to deal with the unique challenges posed by text output and long input sequences.

#Handling Text Output

One of the significant challenges in generative models is that they produce text as output. Traditional attribution algorithms rely on numerical functions to measure how different inputs influence the output. To address this, MExGen introduces a concept called "scalarizers." Scalarizers are functions that convert text outputs into numerical values. This transformation enables the use of attribution algorithms, which can then assign scores to parts of the input based on their contribution to the text output.

#Techniques for Long Inputs

Long input sequences are another hurdle for explanation methods. When summarizing large documents or answering complex questions, the input length can be overwhelming. MExGen overcomes this issue in several ways.

1. Linguistic Segmentation: The input text is split into smaller linguistic units, such as paragraphs, sentences, phrases, and individual words. This segmentation takes advantage of the natural structure of the language and allows for more manageable analysis.

2. Multi-level Explanations: MExGen uses a strategy to attribute scores starting from larger segments (like sentences) and refining down to smaller segments (like phrases or words). This helps control the amount of information being processed and makes explanations clearer.

3. Linear Complexity Algorithms: MExGen employs algorithms that scale linearly with the number of input units. This means that as the input length increases, the computational cost does not rise dramatically, making it efficient for long text inputs.

#Evaluating MExGen

MExGen was tested on tasks such as summarization and question answering. For summation tasks, two well-known datasets were utilized. Additionally, a popular dataset for question answering was selected for evaluation.

Results from the evaluation indicated that MExGen provided more accurate explanations of the generated outputs compared to existing methods. The framework showed a preference for the input parts most relevant to the model's output, making it easier for users to understand how the model arrived at its conclusions.

#Comparison with Existing Methods

MExGen was compared with other explanation methods, such as PartitionSHAP and CaptumLIME. The comparisons were thorough, assessing the performance of MExGen across different tasks and models. MExGen consistently demonstrated superior performance, especially in identifying important tokens in the input text.

#User Studies

To further assess the effectiveness of MExGen, user studies were conducted. Participants viewed various explanations produced by different methods and provided feedback on their perceived fidelity, preference, and clarity. The results revealed that many users found MExGen's explanations more helpful and easier to interpret than those from existing methods.

#Limitations and Future Directions

While MExGen shows promise, there are limitations to consider. First, it's essential to note that MExGen provides post hoc explanations. This means explanations are generated after the model has produced its output, which may not reflect the complete reasoning process of the model.

Second, the evaluations used specific models and datasets. Although the framework performed well in these contexts, variations in other settings could lead to different results. Future studies could explore a more extensive range of models and tasks to confirm the findings.

Lastly, while the user studies were insightful, they primarily focused on user perceptions. More research might be needed to investigate the actual fidelity of the explanations produced by MExGen.

#Conclusion

MExGen offers a valuable contribution to understanding generative language models. By addressing the unique challenges of text outputs and long inputs, this framework improves the quality of explanations available to users. As generative models continue to be integrated into various applications, the need for clear and trustworthy explanations will only grow. MExGen helps fulfill that need, paving the way for more transparent AI systems in the future.

#References

• No references included.