Papers with data construction process
Explainable CED: A Dataset for Explainable Critical Error Detection in Machine Translation (2024.naacl-srw)
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| Challenge: | Existing studies of critical error detection lack content addressing the causes of catastrophic errors. |
| Approach: | They propose a dataset that introduces the attributes of error explanation and correction regarding critical errors. |
| Outcome: | The proposed dataset reduces time costs and mitigates human annotation bias. |
MedDistant19: Towards an Accurate Benchmark for Broad-Coverage Biomedical Relation Extraction (2022.coling-1)
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| Challenge: | Relation extraction in the biomedical domain is challenging due to the lack of labeled data and high annotation costs. |
| Approach: | They propose to use distant supervision to pair knowledge graph relationships with raw texts to tackle the scarcity of annotated data and to validate their results. |
| Outcome: | The proposed benchmarks are more accurate and consistent with existing benchmarks and show that there is no train-test leakage. |
Instance Regularization for Discriminative Language Model Pre-training (2022.emnlp-main)
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| Challenge: | Existing studies have optimized independent strategies of ennoising or denosing . Existing methods treat training instances equally throughout the training process . |
| Approach: | They propose to use ennoising and denoising to train discriminative pre-trained language models . they propose to model the complexity of restoring the original sentences from corrupted ones . |
| Outcome: | Experimental results show that the proposed method improves pre-training efficiency, effectiveness, and robustness. |
Step-by-Step Mastery: Enhancing Soft Constraint Following Ability of Large Language Models (2025.findings-acl)
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| Challenge: | In real-world scenarios, user instructions often contain soft constraints, which are semantically related and cannot be rule-based verified, posing challenges for large language models. |
| Approach: | They propose a pipeline to construct datasets with high-quality outputs for instructions containing soft constraints automatically and use Direct Preference Optimization (DPO) as the training method. |
| Outcome: | The proposed model improves the LLMs' soft constraint following ability by using direct preference optimization (DPO) and constraint quantity. |