Papers by Ran Xin
Progra: Progress-Aware Reinforcement Learning for Multi-Turn Function Calling (2026.findings-acl)
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Huacan Chai, Zijie Cao, Maolin Ran, Yingxuan Yang, Jianghao Lin, Xin Peng, Hairui Wang, Renjie Ding, Ziyu Wan, Muning Wen, Weiwen Liu, Weinan Zhang, Fei Huang, Ying Wen
| Challenge: | Existing methods for multi-turn function calling are limited by redundancy and lack explicit integration of progress awareness into training. |
| Approach: | They propose a framework that explicitly integrates progress awareness into LLM training for multi-turn function calling. |
| Outcome: | Empirical results show that Progra outperforms existing methods on two public benchmarks. |
Can Language Models Capture Human Writing Preferences for Domain-Specific Text Summarization? (2025.findings-acl)
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| Challenge: | Recent studies employ large language models as auxiliary tools for humancentered NLP. |
| Approach: | They construct a model to capture human writing preferences by fine-tuning pre-trained models with data and designing prompts to optimize the output of large language models. |
| Outcome: | The proposed model captures human writing preferences through the dimensions of length, content depth, tone & style, and summary format. |
RepoGenesis: Benchmarking End-to-End Microservice Generation from Readme to Repository (2026.acl-long)
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Zhiyuan Peng, Xin Yin, Pu Zhao, Fangkai Yang, Lu Wang, Ran Jia, Xu Chen, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Existing benchmarks focus on isolated function/class-level generation, neglecting complete microservice repository generation. |
| Approach: | They propose a multilingual benchmark for repository-level end-to-end web microservice generation that reflects real-world development workflows. |
| Outcome: | The benchmark compared 106 repositories across 18 domains and 11 frameworks and 1,258 API endpoints and 2,335 test cases. |
ConNER: Consistency Training for Cross-lingual Named Entity Recognition (2022.emnlp-main)
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| Challenge: | Existing consistency training methods for named entity recognition (NER) are likely to violate the consistency hypothesis or focus on coarse-grain consistency. |
| Approach: | They propose a consistency training framework for cross-lingual named entity recognition that leverages unlabeled target-language data and dropout-based consistency training on labeled source-language datasets. |
| Outcome: | The proposed framework improves on translation-based consistency training on unlabeled target-language data and dropout-based consistent training on labeled source-language datasets. |
MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER (2022.acl-long)
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| Challenge: | Named entity recognition (NER) tasks have limited amount of labeled data . data augmentation methods suffer from token-label misalignment, which leads to unsatsifactory performance. |
| Approach: | They propose a data augmentation framework that explicitly injects NER labels into sentence context and generates high-quality augmented data with novel entities. |
| Outcome: | The proposed framework outperforms baseline methods on low-resource tasks. |
Self-Supervised Sentence Polishing by Adding Engaging Modifiers (2023.acl-demo)
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| Challenge: | a typical way to polish sentences is to add engaging modifiers, which enhance the meaning of the sentence. |
| Approach: | They propose a task that requires polishing sentences while maintaining fluency . they remove engaging modifiers from public resources and fine-tune LongLM to reconstruct original sentences from corrupted ones. |
| Outcome: | The proposed model generates more engaging sentences with suitable modifiers than strong baselines while keeping fluency. |
Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning (2023.acl-long)
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| Challenge: | Existing methods to bridge the linguistic gap between self-training and monolingual named entity recognition (NER) however, due to sub-optimal performance on target languages, the pseudo labels are noisy and limit the overall performance. |
| Approach: | They propose to combine representation learning and pseudo label refinement in one coherent framework to improve self-training for cross-lingual named entity recognition (NER) |
| Outcome: | The proposed method improves cross-lingual named entity recognition (NER) on multiple transfer pairs. |
NeuralClassifier: An Open-source Neural Hierarchical Multi-label Text Classification Toolkit (P19-3)
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| Challenge: | NeuralClassifier is a toolkit for hierarchical multi-label text classification. |
| Approach: | They propose a toolkit for neural hierarchical multi-label text classification . they use a variety of text encoders to implement the model . |
| Outcome: | The proposed model achieves comparable performance with reported results in the literature. |
BFS-Prover: Scalable Best-First Tree Search for LLM-based Automatic Theorem Proving (2025.acl-long)
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| Challenge: | Existing approaches to theorem proving in large language models rely on value functions and/or Monte Carlo Tree Search (MCTS), but the potential of simpler methods like Best-First Tree Search remains underexplored. |
| Approach: | They propose a scalable expert iteration framework that implements strategic data filtering at each expert iteration round, excluding problems solvable via beam search node expansion to focus on harder cases. |
| Outcome: | The proposed framework achieves a state-of-the-art score of 72.95 on the MiniF2F test set and challenges the perceived necessity of complex tree search methods. |
JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs (2021.findings-acl)
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| Challenge: | Existing pre-trained models for knowledgegraph-to-text generation ignore graph structure during encoding and lack elaborate pre-training tasks to explicitly model graph-text alignments. |
| Approach: | They propose a graph-text joint representation learning model called JointGT which incorporates a structure-aware semantic aggregation module into each Transformer layer to preserve the graph structure. |
| Outcome: | The proposed model achieves state-of-the-art performance on various KG-to-text datasets. |
STANKER: Stacking Network based on Level-grained Attention-masked BERT for Rumor Detection on Social Media (2021.emnlp-main)
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| Challenge: | Existing models for text classification are limited in performance, resulting in poor rumor detection. |
| Approach: | They propose to use Chinese microblogs to detect rumors using pre-trained language models and auxiliary features such as comments to mask co-attention. |
| Outcome: | The proposed model outperforms the state-of-the-art on Weibo20 and three existing social media datasets. |