Papers with reconstruction process
Instruction-Tuning Data Synthesis from Scratch via Web Reconstruction (2025.findings-acl)
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Yuxin Jiang, Yufei Wang, Chuhan Wu, Xinyi Dai, Yan Xu, Weinan Gan, Yasheng Wang, Xin Jiang, Lifeng Shang, Ruiming Tang, Wei Wang
| Challenge: | Existing methods for generating and curating high-quality instruction-tuning data rely heavily on the quality of seed data or strong assumptions about the structure and content of web documents. |
| Approach: | They propose a fully automated framework for synthesizing high-quality instruction-tuning (IT) data directly from raw web documents with minimal assumptions. |
| Outcome: | The proposed framework outperforms state-of-the-art baselines by 16.65% across four instruction-following benchmarks. |
Learning Universal Sentence Representations with Mean-Max Attention Autoencoder (D18-1)
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| Challenge: | Existing methods to learn universal sentence representations focus on supervised learning. |
| Approach: | They propose a mean-max attention autoencoder that uses a multi-head mechanism to reconstruct the input sequence. |
| Outcome: | The proposed model outperforms state-of-the-art unsupervised single methods on a wide range of 10 transfer tasks. |
Reconstruct Before Summarize: An Efficient Two-Step Framework for Condensing and Summarizing Meeting Transcripts (2023.emnlp-main)
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| Challenge: | Existing approaches to meeting summarization are limited due to noise, lengthy transcripts, and scattered salient information. |
| Approach: | They propose a two-step framework for meeting summarization that leverages a self-supervised paradigm to reconstruct transcripts and a relative positional bucketing algorithm to equip models to generate the summary. |
| Outcome: | The proposed method significantly reduces memory consumption and processing time on two meeting summarization datasets. |
Releasing the Capacity of GANs in Non-Autoregressive Image Captioning (2024.lrec-main)
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| Challenge: | Existing non-autoregressive (NAR) models suffer from their inherent multi-modality problem. |
| Approach: | They propose an Adversarial Non-autoregressive Transformer for Image Captioning that improves model performance by modifying model structure to be compatible with contrastive learning. |
| Outcome: | The proposed model achieves 26.72 times faster than the autoregressive model on the MSCOCO dataset. |