Papers by Yingqi Cao
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. |