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Context: Once the the most important heads are identified, their attention distributions are inspected to look for patterns. In this work, we propose and test a novel human-in-the-loop pipeline that to the best of our knowledge is the first attemptto combine research on analyzing self-attention with work on injecting patterns into attention matrices.To start, human users visually explore the attention matrices of transformers to identify task-specific patterns that could be formalized as a predicate. After quantitatively evaluating the patterns on the validation set, they can be injected into attention heads of transformer models to simultaneously improve task accuracy and make the model more efficient by sparsifying the attention matrices111The implementation of our work is publicly available at: https://github.com/raymondzmc/Attention-Pattern-Exploitation. This is in contrast to previous work that mostly focuses on the trade-off between two metrics. To discovertask-specific patterns,we analyze the top-3 most important heads of each layer, and look forhuman-interpretable relationships encoded in the attention weights. In practice, we use the instance-level interactions provided by the visual framework (Li et al., 2021), and randomly select 5 validation examples per task for our analysis.The entire process takesless than one hour to complete for each task, where we manually examinethe attention weights for less than half of the tokens for each example.It is worth noting that detailed analysis regarding the trade-off between human costandpattern recallwould require extensive user studies beyond the scope of this work. As future work, we plan to apply our pipeline to other NLP tasks (e.g. language modeling, abstractive summarization) and explore and verify whether the important patterns from one task can be transferable to another task. Similarly, we also plan to apply our pipeline to different model variants to examine and compare the patterns encoded in the attention weights.In the long term, our pipeline could be naturally automated by replacing the pattern discovery step with evaluating predefined linguistic patterns. However, assessing the efficiency gains from injecting such patterns (requiring ground-truth annotations) would require more in-depth studies beyond the scope of this paper.Finally, since human factors are an important aspect of interpretability, we plan to conduct extensive user studies across different NLP tasks and model sizes to examine the trade-off between human-cost and the coverage of discovered patterns.

Question: Instead of automatically identifying important attention patterns, why should a human be involved in this process?
Human involvement can provide interpretable results from the identified patterns and the performance enhancement from the pattern injection.