Papers with model memorization
DEPN: Detecting and Editing Privacy Neurons in Pretrained Language Models (2023.emnlp-main)
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| Challenge: | Existing studies have demonstrated that pretrained language models memorize and regurgitate a significant portion of training data, including atypical data points that appear only once in the training data. |
| Approach: | They propose a method to locate and erase risky neurons in order to eliminate the impact of privacy data in the model in batches. |
| Outcome: | The proposed method eliminates the impact of privacy data in the model in batches without affecting the model's performance. |
HMT: Hierarchical Memory Transformer for Efficient Long Context Language Processing (2025.naacl-long)
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| Challenge: | Existing models that memorize past tokens have “flat” memory architectures that restrict the context window. |
| Approach: | They propose a framework that imitates human memorization behavior by preserving tokens from early input segments, passing memory embeddings along the sequence, and recalling relevant information from history. |
| Outcome: | The proposed framework outperforms existing models in language modeling and question-answering tasks and achieves comparable or superior generation quality to long-context models with 2 57 fewer parameters and 2.5 116 less inference memory. |
Quantifying Contamination in Evaluating Code Generation Capabilities of Language Models (2024.acl-long)
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| Challenge: | Recent studies have shown that large language models are contaminated with data from pretraining and finetuning tasks. |
| Approach: | They perform extensive analysis on the factors that affect model memorization and generalization, such as model size, problem difficulty, and question length. |
| Outcome: | The results show that models perform better on the subset of the benchmarks where similar solutions are seen during training. |