Papers with generation and
Constructing Open Cloze Tests Using Generation and Discrimination Capabilities of Transformers (2022.findings-acl)
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| Challenge: | Existing open cloze tests are laborious to design because they require a large number of variables to predict the distribution of words in a text passage. |
| Approach: | They propose a transformer-based model that exploits generation and discrimination capabilities to improve performance. |
| Outcome: | The proposed model outperforms previous work and baselines in 82% accuracy and can be used as a future benchmark. |
Alleviating Distribution Shift in Synthetic Data for Machine Translation Quality Estimation (2025.acl-long)
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| Challenge: | Quality Estimation (QE) models evaluate the quality of machine translations without reference translations, serving as the reward models for the translation task. |
| Approach: | They propose a framework for alleviating distribution shift in synthetic QE data . they employ a constrained beam search algorithm and distinct generation models to enhance translation diversity. |
| Outcome: | The proposed framework outperforms SOTA baselines like CometKiwi in supervised and unsupervised settings. |
Prompt Optimization via Adversarial In-Context Learning (2024.acl-long)
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Do Long, Yiran Zhao, Hannah Brown, Yuxi Xie, James Zhao, Nancy Chen, Kenji Kawaguchi, Michael Shieh, Junxian He
| Challenge: | Existing methods to optimize prompts for in-context learning are based on adversarial learning and are computationally efficient and extensible to other LLMs and tasks. |
| Approach: | They propose a method to optimize prompts for in-context learning by a generator and a discriminator. |
| Outcome: | The proposed method improves state-of-the-art prompt optimization techniques on 13 generation and classification tasks including summarization, arithmetic reasoning, machine translation, data-to-text generation, and the MMLU and big-bench hard benchmarks. |
Incorporating Domain Knowledge into Materials Tokenization (2025.acl-long)
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| Challenge: | Recent advances in language models have expanded their applications in materials science, but they often produce excessive fragmentation and semantic loss. |
| Approach: | They propose a frequency-centric tokenization approach that integrates material knowledge into tokenization. |
| Outcome: | The proposed tokenization approach outperforms existing tokenization methods and achieves an average performance gain of 4% and 2% in the generation and classification tasks. |
Investigating How Pre-training Data Leakage Affects Models’ Reproduction and Detection Capabilities (2025.emnlp-main)
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| Challenge: | Existing studies do not examine how leaked instances in training datasets influence LLMs’ output and detection capabilities. |
| Approach: | They conduct an experimental survey to examine the relationship between data leakage in training datasets and its effects on the generation and detection by Large Language Models (LLMs). |
| Outcome: | The results show that enhancing leakage detection through few-shot learning can help mitigate the impact of the leakage rate in the training data on detection performance. |