Papers with encoder-only
HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information (2022.findings-acl)
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| Challenge: | Existing models that treat texts as linear sequences do not include hierarchical structure information. |
| Approach: | They propose to inject hierarchical structure information into an extractive summarization model by combining hierarchically structured text with a pre-trained Transformer language model. |
| Outcome: | The proposed model outperforms a baseline model on PubMed and arXiv datasets and the hierarchical structure information is not injected. |
Representing Lean Proofs as Trajectories in Latent Space (2026.acl-srw)
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| Challenge: | Lean proofs are built as sequences of tactic-induced state transitions, but learned models often represent proof steps through tactic strings or raw proof-state text. |
| Approach: | They train an encoder-only Transformer to learn contextualized representations of Lean proof steps from state changes. |
| Outcome: | The proposed model yields better held-out next-tactic retrieval than a surface-syntax control . the results provide a promising basis for future trajectory-aware theorem proving . |
ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference (2022.findings-acl)
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Kai Hui, Honglei Zhuang, Tao Chen, Zhen Qin, Jing Lu, Dara Bahri, Ji Ma, Jai Gupta, Cicero Nogueira dos Santos, Yi Tay, Donald Metzler
| Challenge: | State-of-the-art neural models typically encode document-query pairs using cross-attention for re-ranking. |
| Approach: | They propose to fine tune a pretrained encoder-decoder model using document to query generation. |
| Outcome: | The proposed model achieves comparable results to more expensive approaches while being 6.8X faster. |
Translation and Fusion Improves Cross-lingual Information Extraction (2025.acl-long)
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| Challenge: | Large language models (LLMs) have shown significant progress in information extraction tasks due to lack of labeled data for fine-tuning and unlabeled text for pre-training. |
| Approach: | They propose a framework in which large language models are fine-tuned to use English translations of low-resource language data. |
| Outcome: | The proposed model improves cross-lingual transfer over the base model on 12 multilingual IE datasets spanning 50 languages. |
Denoising based Sequence-to-Sequence Pre-training for Text Generation (D19-1)
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| Challenge: | PoDA pre-trains encoders and decoders by denoising noise-corrupted text . Unlike encoder-only or decode-only methods, it can be used for text generation tasks without using any task-specific techniques. |
| Approach: | They propose a sequence-to-sequence (seq2sequ) pre-training method PoDA which denoises autoencoders by denoising noise-corrupted text. |
| Outcome: | The proposed method improves model performance over strong baselines without using any task-specific techniques and significantly speed up convergence. |
Knowledge Fusion By Evolving Weights of Language Models (2024.findings-acl)
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| Challenge: | Experimental results on mainstream language models show that Evolver outperforms previous state-of-the-art models by large margins due to the high training costs of large language models. |
| Approach: | They propose a method to integrate multiple models from diverse training scenarios into a unified model. |
| Outcome: | The proposed method outperforms state-of-the-art models on mainstream language models by large margins. |
Exploring Large Language Models for Classical Philology (2023.acl-long)
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| Challenge: | Recent advances in NLP have led to the creation of powerful language models for many languages including Ancient Greek and Latin. |
| Approach: | They propose to use encoder-only and encoder decoder architectures to create four models for Ancient Greek that vary along two dimensions for tasks of interest for Classical languages. |
| Outcome: | The proposed models improve on existing models of Ancient Greek and Latin and provide a large pre-training corpus for Ancient Greek to support the creation of a larger, comparable model zoo for Classical Philology. |
Are Decoder-Only Language Models Better than Encoder-Only Language Models in Understanding Word Meaning? (2024.findings-acl)
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| Challenge: | Large language models are highly effective tools for solving different kinds of problems in natural language processing. |
| Approach: | They propose to use large language models to solve a myriad of problems. |
| Outcome: | The proposed model performs worse on word meaning comprehension than an encoder-only model with vastly fewer parameters. |
Sinkhorn Distance Minimization for Knowledge Distillation (2024.lrec-main)
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Xiao Cui, Yulei Qin, Yuting Gao, Enwei Zhang, Zihan Xu, Tong Wu, Ke Li, Xing Sun, Wengang Zhou, Houqiang Li
| Challenge: | Existing knowledge distillation methods investigate divergence measures but fail to deliver effective supervision when few distribution overlap exists between teacher and student. |
| Approach: | They propose a knowledge distillation method that exploits the Sinkhorn distance to ensure a nuanced assessment of the disparity between teacher and student distributions. |
| Outcome: | The proposed method outperforms state-of-the-art methods on all kinds of LLMs with encoder-only, encoder decoder, and decoded architectures. |