Papers by Qian Ruan
Are Large Language Models Good Classifiers? A Study on Edit Intent Classification in Scientific Document Revisions (2024.emnlp-main)
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| Challenge: | Generative large language models (LLMs) have brought advances in text generation, but their potential for enhancing classification tasks remains underexplored. |
| Approach: | They propose a framework for thoroughly investigating fine-tuning LLMs for classification . they instantiate this framework in edit intent classification (EIC) a challenging and underexplored classification task. |
| Outcome: | The proposed framework is applied to edit intent classification (EIC) The proposed methods are generalizable on five further classification tasks. |
Re3: A Holistic Framework and Dataset for Modeling Collaborative Document Revision (2024.acl-long)
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| Challenge: | a framework for collaborative document revision is lacking for empirical analysis and NLP. |
| Approach: | They propose a framework for joint analysis of collaborative document revision that instantiates a corpus of aligned scientific paper revisions manually labeled according to their action and intent. |
| Outcome: | The proposed framework provides first empirical insights into collaborative document revision in the academic domain and assesses its capabilities. |
Generating Extended and Multilingual Summaries with Pre-trained Transformers (2022.lrec-1)
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| Challenge: | Almost all summarisation methods focus on a single language and short summaries. |
| Approach: | They propose a dataset for extended summarisation tailored for 11 sentences . they compare three multilingual transformer models on extractive and abstractive summarization tasks . |
| Outcome: | The proposed dataset is tailored for extended summaries of approx. 11 sentences. |
HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information (2022.findings-acl)
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| Challenge: | Existing models that treat texts as linear sequences do not include hierarchical structure information. |
| Approach: | They propose to inject hierarchical structure information into an extractive summarization model by combining hierarchically structured text with a pre-trained Transformer language model. |
| Outcome: | The proposed model outperforms a baseline model on PubMed and arXiv datasets and the hierarchical structure information is not injected. |
Author-in-the-Loop Response Generation and Evaluation: Integrating Author Expertise and Intent in Responses to Peer Review (2026.acl-long)
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| Challenge: | Existing ARG work lacks author inputs and controls and no evaluation measures response reflection of author signals and effectiveness in addressing reviewer concerns. |
| Approach: | They propose a novel author-in-the-loop framework that integrates domain expertise and author-only information into author response generation (ARG) they also propose re3Align, a large-scale dataset of aligned review–response–revision triplets, where revisions proxy author signals and REspGen, an author- in-the loop ARG framework supporting flexible author input, multi-attribute control, and evaluation-guided refinement. |
| Outcome: | Experiments with SOTA LLMs show that author input and evaluation-guided refinement improves author response quality and controllability–quality trade-offs. |