Papers by Shufang Xie
Data Pollination: An Emergent Ecological Process Driving AI Population Evolution (2026.acl-long)
Copied to clipboard
| Challenge: | evidence from deployed systems suggests that language models interact through a shared data ecosystem. |
| Approach: | They propose to use data pollination to investigate stability dynamics under synthetic data training to investigate model collapse. |
| Outcome: | The proposed model can mitigate model collapse observed in recursive training, and improve performance across benchmarks. |
MolXPT: Wrapping Molecules with Text for Generative Pre-training (2023.acl-short)
Copied to clipboard
| Challenge: | Experimental results show that Generative pre-trained Transformers (GPT) have great success in natural language processing. |
| Approach: | They propose a unified language model of text and molecules pre-trained on SMILES wrapped by text. |
| Outcome: | The proposed model outperforms strong baselines of molecular property prediction on MoleculeNet and performs comparably to the best model in text-molecule translation while using less than half of its parameters. |
Building Multilingual Machine Translation Systems That Serve Arbitrary XY Translations (2022.naacl-main)
Copied to clipboard
| Challenge: | Multilingual Neural Machine Translation (MNMT) systems are often limited to many-to-one directions and suffer from poor performance in one-to one directions. |
| Approach: | They propose to build multilingual machine translation systems that serve arbitrary X-Y directions while leveraging multilinguality with a two-stage training strategy of pretraining and finetuning. |
| Outcome: | The proposed system outperforms baseline bilingual models and pivot translation models in most directions without the need for architecture change or extra data collection. |
SciCustom: A Framework for Custom Evaluation of Scientific Capabilities in Large Language Models (2026.acl-long)
Copied to clipboard
Yiyang Gu, Junwei Yang, Junyu Luo, Ye Yuan, Bin Feng, Yingce Xia, Shufang Xie, Kaili Liu, Bohan Wu, Qi Shi, Haoran Li, Beier Xiao, Zhiping Xiao, Xiao Luo, Weizhi Zhang, Philip S. Yu, Zequn Liu, Ming Zhang
| Challenge: | Existing evaluations of large language models fail to reflect fine-grained capabilities . existing benchmarks are manually curated or domain-generic, limiting scalability and alignment with real use cases. |
| Approach: | They propose a framework that allows custom construction of benchmarks from large-scale scientific data to evaluate application-specific scientific capabilities in LLMs. |
| Outcome: | The proposed framework reveals fine-grained differences in scientific capabilities that standard benchmarks overlook . it allows custom construction of benchmarks from large-scale scientific data to evaluate application-specific capabilities in LLMs. |
An Analysis and Mitigation of the Reversal Curse (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent research observes a phenomenon in large language models called the "reversal curse" when dealing with two entities, LLMs excel in handling sequences in the form of "aRb" but when asked "who is Mary Lee Pfeiffer's son?" the LLM exhibits considerable confusion and fails to provide a as the answer . |
| Approach: | They conduct the first-ever study of how the reversal curse happens in large language models . they find that LLMs excel in handling sequences in the form of "aRb" but struggle to provide a satisfactory answer when asked "who is Mary Lee Pfeiffer's son?" |
| Outcome: | The proposed study shows that the reversal curse can stem from specific training objectives . the study also shows that a reverse query can be difficult to understand . |
Envisioning Future from the Past: Hierarchical Duality Learning for Multi-Turn Dialogue Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to generate high quality responses rely on future text . |
| Approach: | They propose a hierarchical duality learning for dialogue to simulate human cognitive ability . they utilize hierarchically dualities at token hierarchy and utterance hierarchy to simulate duality . |
| Outcome: | The proposed model can generate high quality responses that connect both previous and follow-up dialogues. |
UniDrop: A Simple yet Effective Technique to Improve Transformer without Extra Cost (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing approaches to improve the performance of natural language processing models are over-parameterized and overfitted. |
| Approach: | They propose an approach to integrate dropout techniques into the training of Transformer models. |
| Outcome: | The proposed approach can achieve 1.5 BLEU improvement on IWSLT14 translation tasks and better accuracy for the classification even using strong pre-trained RoBERTa as backbone. |
What are the Desired Characteristics of Calibration Sets? Identifying Correlates on Long Form Scientific Summarization (2023.acl-long)
Copied to clipboard
Griffin Adams, Bichlien Nguyen, Jake Smith, Yingce Xia, Shufang Xie, Anna Ostropolets, Budhaditya Deb, Yuan-Jyue Chen, Tristan Naumann, Noémie Elhadad
| Challenge: | Summarization models are trained to maximize the likelihood of a single reference (MLE) but little is known about why one setup is more effective than another . |
| Approach: | They add a calibration step which exposes a model to its own ranked outputs to improve relevance or contrasts positive and negative sets to improve faithfulness. |
| Outcome: | The proposed calibration step can unlock large gains in relevance or faithfulness. |
Extract and Attend: Improving Entity Translation in Neural Machine Translation (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to improve entity translation in Neural machine translation still suffer from inaccurate translation of entities due to the lack of entity training instances. |
| Approach: | They propose an extract-and-tend approach to enhance entity translation in NMT by extracting entities from a dictionary and attending to them with a prefix. |
| Outcome: | Experiments on En-Zh and En-Ru show that the proposed approach improves translation accuracy and translation quality. |
BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning (2024.findings-acl)
Copied to clipboard
Qizhi Pei, Lijun Wu, Kaiyuan Gao, Xiaozhuan Liang, Yin Fang, Jinhua Zhu, Shufang Xie, Tao Qin, Rui Yan
| Challenge: | BioT5+ is an extension of the BioT5, but lacked a nuanced understanding of molecular structures. |
| Approach: | They propose a new bio-entity modeling framework, BioT5+, which integrates IUPAC names and molecule data. |
| Outcome: | The proposed model bridges the gap between molecular representations and textual descriptions and improves the grounded reasoning of bio-text and bio-sequences. |