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. |
| Approach: | They propose to improve MEMIT's model editing algorithm by varying edit tokens and improving the layer selection strategy to improve commonsense knowledge. |
| Outcome: | The MEMIT editing algorithm outperforms baseline models on PEP3k and 20Q datasets while fine-tuning baselines shows significant trade-offs. |
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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. |
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One Mask to Rule Them All: On Hidden Facts after Editing and How to Find Them (2026.findings-acl)
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| Challenge: | Knowledge editing methods such as ROME and MEMIT update factual associations by modifying MLP weights. |
| Approach: | They propose to use a mask to reverse edits by eliminating overattention in later layers . they also show that injecting the mask during editing drops editing success from 98% to 38% . |
| Outcome: | The proposed method reverses edits by eliminating overattention in later layers and drops editing success from 98% to 38%. |
Mass-Editing Memory with Attention in Transformers: A cross-lingual exploration of knowledge (2024.findings-acl)
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| Challenge: | Recent studies have explored methods for updating and modifying factual knowledge in large language models, often focusing on specific multi-layer perceptron blocks. |
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Do Transformer Modifications Transfer Across Implementations and Applications? (2021.emnlp-main)
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Sharan Narang, Hyung Won Chung, Yi Tay, Liam Fedus, Thibault Fevry, Michael Matena, Karishma Malkan, Noah Fiedel, Noam Shazeer, Zhenzhong Lan, Yanqi Zhou, Wei Li, Nan Ding, Jake Marcus, Adam Roberts, Colin Raffel
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CK-Transformer: Commonsense Knowledge Enhanced Transformers for Referring Expression Comprehension (2023.findings-eacl)
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| Challenge: | Existing frameworks for referring expression comprehension with commonsense knowledge are lacking in the field of multimodal referring . |
| Approach: | They propose a framework for commonsense knowledge Enhanced Transformers which integrates commonsensible knowledge into representations of objects in an image. |
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TARE: Lightweight Token-Aware Representation Editing for Fine-tuning Transformer-like Models (2026.acl-long)
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| Challenge: | Existing PEFT methods can be costly and underfit token-level contexts. |
| Approach: | They propose a PEFT method that performs fine-grained, token-specific edits with a small additional inference overhead and minimal tuning. |
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Unravelling the Logic: Investigating the Generalisation of Transformers in Numerical Satisfiability Problems (2025.acl-long)
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Transformer-specific Interpretability (2024.eacl-tutorials)
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| Challenge: | Transformers are dominant play-ers in various scientific fields, but their inner workings remain opaque. |
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Identifying the limits of transformers when performing model-checking with natural language (2023.eacl-main)
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| Challenge: | Recent studies have focused on transformer models’ ability to perform reasoning on text, but the above question has not been adequately answered. |
| Approach: | They investigated the problem of model-checking with natural language to determine whether transformers can comprehend logical semantics in natural language. |
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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. |