Papers with data construction method
ControlAudio: Tackling Text-Guided, Timing-Indicated and Intelligible Audio Generation via Progressive Diffusion Modeling (2026.acl-long)
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| Challenge: | Recent efforts on text-to-audio generation are exploring fine-grained controllability . however, their performance at scale is limited due to data scarcity . |
| Approach: | They propose a multi-task learning problem for high-controllability text-to-audio generation . they propose scalable diffusion transformers that augment condition information in sequence . |
| Outcome: | The proposed method outperforms existing methods on objective and subjective evaluations. |
Improving Commonsense Contingent Reasoning by Pseudo-data and Its Application to the Related Tasks (2022.coling-1)
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| Challenge: | Contingent reasoning is one of the essential abilities in natural language understanding . despite advances in deep learning, the task of contingent reasoning is still difficult for computers . |
| Approach: | They propose to generate large-scale pseudo-problems and incorporate them into training . they also investigate the generality of contingent knowledge through quantitative evaluation . |
| Outcome: | The proposed method is able to evaluate the generality of contingent knowledge through transfer learning. |
Lying Through One’s Teeth: A Study on Verbal Leakage Cues (2021.emnlp-main)
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| Challenge: | Existing studies on verbal leakage cues do not address their impact on models' validity. |
| Approach: | They propose to use LIWC to show verbal leakage cues in lie detection datasets to understand their effect on data collection and examine their validity. |
| Outcome: | The proposed models with more strong verbal leakage cue categories perform better than models trained on a dataset with only a greater number of strong cues. |
Enhancing Legal Case Retrieval via Scaling High-quality Synthetic Query-Candidate Pairs (2024.emnlp-main)
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| Challenge: | Existing studies focus on case-to-case retrieval using lengthy queries, which does not match real-world scenarios. |
| Approach: | They propose a method to construct query-candidate pairs and build the largest LCR dataset to date, LEAD. |
| Outcome: | Experimental results show that the method can provide ample training signals for LCR models. |
UltraLink: An Open-Source Knowledge-Enhanced Multilingual Supervised Fine-tuning Dataset (2024.acl-long)
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Haoyu Wang, Shuo Wang, Yukun Yan, Xujia Wang, Zhiyu Yang, Yuzhuang Xu, Zhenghao Liu, Liner Yang, Ning Ding, Xu Han, Zhiyuan Liu, Maosong Sun
| Challenge: | Open-source large language models (LLMs) have gained strength across diverse fields, but the majority of studies focus on English. |
| Approach: | They propose a knowledge-grounded data augmentation approach to elicit more language-specific knowledge of LLMs by enhancing their ability to serve users from different countries. |
| Outcome: | The proposed method can prune the language-agnostic supervised fine-tuning dataset without any performance degradation. |