Papers with sequence-level training
Classical Structured Prediction Losses for Sequence to Sequence Learning (N18-1)
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| Challenge: | Recent work on training neural attention models at the sequence level has focused on a series of objective functions commonly used for structured prediction. |
| Approach: | They propose to use objective functions commonly used to train linear models for structured prediction to train neural attention models at the sequence-level using either reinforcement learning-style methods or beam search optimization. |
| Outcome: | The proposed model outperforms beam search optimization on German-English translation and abstractive summarization tasks. |
Training for Diversity in Image Paragraph Captioning (D18-1)
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| Challenge: | Existing image captioning models have a lack of diversity between sentences . current models have limited their effectiveness due to repetitive paragraphs . |
| Approach: | They propose to apply sequence-level training to image paragraph captioning models . they find that standard self-critical training produces poor results . |
| Outcome: | The proposed training improves on the Visual Genome dataset with no architectural changes. |
Understanding and Improving Information Preservation in Prompt Compression for LLMs (2025.findings-emnlp)
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| Challenge: | Recent advances in large language models have enabled their successful application to a broad range of tasks. |
| Approach: | They propose a framework that allows for in-depth analysis of prompt compression methods. |
| Outcome: | The proposed framework analyzes state-of-the-art soft and hard compression methods . it shows that some fail to preserve key details from the original prompt, limiting performance on complex tasks. |