| Challenge: | Existing methods for training memory-augmented language models only introduce mem-ories at testing time or represent them using a separately trained encoder. |
| Approach: | They propose a training approach that directly takes in-batch examples as accessible memory and new methods for memory construction and data batching that are used for adapting to different sets of memories at testing time. |
| Outcome: | The proposed approach reduces perplexity from 18.70 to 15.37 on multiple language modeling and machine translation benchmarks. |
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| Challenge: | Prior work on retrieval augmentation fine-tuned the retriever and the LM, making them closely coupled. |
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Mini-Model Adaptation: Efficiently Extending Pretrained Models to New Languages via Aligned Shallow Training (2023.findings-acl)
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| Challenge: | Existing approaches to pretrain Masked Language Models (MLMs) are expensive and require a full forward and backward pass over the entire model. |
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Empowering Large Language Models for Textual Data Augmentation (2024.findings-acl)
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| Challenge: | True. True. False |
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How to Make LMs Strong Node Classifiers? (2026.findings-eacl)
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| Challenge: | Large language models for machine translation often face difficulties in leveraging demonstrations to further improve their performance. |
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CUE Vectors: Modular Training of Language Models Conditioned on Diverse Contextual Signals (2022.findings-acl)
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E2-LLM: Efficient and Extreme Length Extension of Large Language Models (2024.findings-acl)
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Jiaheng Liu, ZhiqiBai ZhiqiBai, Yuanxing Zhang, Chenchen Zhang, YuangZh YuangZh, Ge Zhang, JiakaiWang JiakaiWang, Haoran Que, Yukang Chen, Wenbo Su, Tiezheng Ge, Jie Fu, Wenhu Chen, Bo Zheng
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