Interpretability-based Tailored Knowledge Editing in Transformers (2024.emnlp-main)
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| Challenge: | Existing methods for modifying in-context learning fail to analyze the instability of in-constitu learning outcomes. |
| Approach: | They propose a model-based knowledge editing method that considers the unique information flow of each sample and aims to correct errors without costly retraining. |
| Outcome: | The proposed method exploits the critical role of feed-forward MLPs in decoder-only models and reveals diverse attribute recall across transformer layers, guiding edits to specific features at different depths and mitigating over-editing issues. |
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| Challenge: | Transformers are dominant play-ers in various scientific fields, but their inner workings remain opaque. |
| Approach: | This tutorial presents a trending approach to interpreting Transformers . it uses specific features of the Transformer architecture to quantify context- mixing interactions . |
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VISIT: Visualizing and Interpreting the Semantic Information Flow of Transformers (2023.findings-emnlp)
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| Challenge: | Recent work in interpretability suggests we can project weights and hidden states of transformer-based language models (LMs) to their vocabulary space, a transformation that makes them more human interpretable. |
| Approach: | They propose a tool to visualize a forward pass of Generative Pre-trained Transformers as an interactive flow graph with nodes representing neurons or hidden states and edges representing interactions between them. |
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Editing Common Sense in Transformers (2023.emnlp-main)
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Anshita Gupta, Debanjan Mondal, Akshay Sheshadri, Wenlong Zhao, Xiang Li, Sarah Wiegreffe, Niket Tandon
| Challenge: | Currently, the performance of transformer-based model editing methods is limited to statements about encyclopedic knowledge with a single correct answer. |
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Towards Opening the Black Box of Neural Machine Translation: Source and Target Interpretations of the Transformer (2022.emnlp-main)
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| Challenge: | Neural Machine Translation (NMT) relies on source sentence and target prefix attributions for each input token. |
| Approach: | They propose an interpretability method that tracks input tokens’ attributions for both contexts and extends it to any encoder-decoder Transformer-based model. |
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DecoderLens: Layerwise Interpretation of Encoder-Decoder Transformers (2024.findings-naacl)
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| Challenge: | Existing interpretability methods have been proposed to interpret the inner workings of Transformer models at different levels of precision and complexity. |
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Choose Your Transformer: Improved Transferability Estimation of Transformer Models on Classification Tasks (2024.findings-acl)
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| Challenge: | Existing models for NLP tasks require fine-tuning, but it is computationally infeasible. |
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InterpreT: An Interactive Visualization Tool for Interpreting Transformers (2021.eacl-demos)
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Vasudev Lal, Arden Ma, Estelle Aflalo, Phillip Howard, Ana Simoes, Daniel Korat, Oren Pereg, Gadi Singer, Moshe Wasserblat
| Challenge: | Using Transformer-based models for NLU/NLP tasks is a growing interest . but there are many open questions regarding the behavior of these models . |
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Ecco: An Open Source Library for the Explainability of Transformer Language Models (2021.acl-demo)
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| Challenge: | Existing models that use the Transformer architecture are lag behind our ability to scale them. |
| Approach: | They propose an open-source library for the explainability of Transformer-based NLP models that captures, analyzes, visualizes, and interactively explores the inner mechanics of these models. |
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What Context Features Can Transformer Language Models Use? (2021.acl-long)
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| Challenge: | Recent studies show that transformer-based language models benefit from conditioning on contexts of hundreds to thousands of previous tokens. |
| Approach: | They propose to use lexical and structural information to ablate usable information in transformer language models. |
| Outcome: | The proposed model improves when conditioning on contexts of thousands of previous tokens. |
On the Sub-layer Functionalities of Transformer Decoder (2020.findings-emnlp)
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| Challenge: | Existing efforts to interpret the encoder of Transformer-based encoder-decoder architectures for neural machine translation have focused on assessing the encoded representations or interpreting the multi-head self-attentions. |
| Approach: | They propose to use Transformer-based encoder-decoder architectures to analyze how information is propagated through each module of each decoder layer. |
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