| Challenge: | Existing methods for interpreting neural network-based language models (LMs) are limited to approximately conditional quantities. |
| Approach: | They introduce a similarity-distance-magnitude activation function and an SDM estimator to control class- and prediction-conditional accuracy among selective classifications. |
| Outcome: | The proposed estimator is more robust to covariate shifts and out-of-distribution inputs while remaining informative over in-difference data. |
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Chenyang Song, Xu Han, Zhengyan Zhang, Shengding Hu, Xiyu Shi, Kuai Li, Chen Chen, Zhiyuan Liu, Guangli Li, Tao Yang, Maosong Sun
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IMPACT: Importance-Aware Activation Space Reconstruction (2026.acl-long)
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| Challenge: | Large language models (LLMs) achieve strong performance across domains but remain difficult to deploy in resource-constrained environments due to their massive size. |
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Activation Scaling for Steering and Interpreting Language Models (2024.findings-emnlp)
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| Challenge: | a successful intervention should flip the correct with the wrong token, while remaining sparse. |
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| Challenge: | Recent studies have suggested that sparse attention mechanisms can be made more interpretable by replacing the softmax activation with its sparser variants. |
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CoLA: Compute-Efficient Pre-Training of LLMs via Low-Rank Activation (2025.emnlp-main)
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Self-Adjust Softmax (2025.emnlp-main)
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Chuanyang Zheng, Yihang Gao, Guoxuan Chen, Han Shi, Jing Xiong, Xiaozhe Ren, Chao Huang, Zhenguo Li, Yu Li
| Challenge: | Usually, tokens with larger attention scores are important for the final prediction. |
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Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019) (D19-61)
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| Challenge: | EMNLP-IJCNLP 2019 Workshop on Deep Learning Approaches for Low-Resource Natural Language Processing takes place in Hong Kong, China . |
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| Challenge: | Recent advances in multimodal large language models have remained opaque. |
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Self-Supervised Document Similarity Ranking via Contextualized Language Models and Hierarchical Inference (2021.findings-acl)
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Breaking ReLU Barrier: Generalized MoEfication for Dense Pretrained Models (2024.emnlp-main)
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| Challenge: | Existing methods to convert pretrained dense models to MoEs are limited to ReLU-based models with natural sparsity. |
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