Papers by Tian Bowen

Rethinking Data Selection at Scale: Random Selection is Almost All You Need (2025.findings-emnlp)

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Tingyu Xia, Bowen Yu, Kai Dang, An Yang, Yuan Wu, Yuan Tian, Yi Chang, Junyang Lin

Challenge:	Existing data selection techniques are designed for small data pools, a study finds . filtering data by token length is an efficient method for improving results .
Approach:	They use self-scoring methods that do not rely on external help to perform fine-tuning . they also find that filtering data by token length offers a stable and efficient method .
Outcome:	The proposed methods outperform random selection on large datasets on large data pools.

QUITO-X: A New Perspective on Context Compression from the Information Bottleneck Theory (2025.findings-emnlp)

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Yihang Wang, Xu Huang, Bowen Tian, Yueyang Su, Lei Yu, Huaming Liao, Yixing Fan, Jiafeng Guo, Xueqi Cheng

Linguistic Theories

Challenge:	Existing methods for compressing context by removing redundant tokens are inconsistent with the objective of retaining the most important tokens when conditioning on a given query.
Approach:	They propose a method that uses information bottleneck theory to compress context . they propose to remove redundant tokens using metrics such as self-information or perplexity .
Outcome:	The proposed method achieves a 25% increase in compression rate compared to the state-of-the-art .

ReviewRL: Towards Automated Scientific Review with RL (2025.emnlp-main)

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Sihang Zeng, Kai Tian, Kaiyan Zhang, Yuru Wang, Junqi Gao, Runze Liu, Sa Yang, Jingxuan Li, Xinwei Long, Jiaheng Ma, Biqing Qi, Bowen Zhou

Challenge:	Existing automated review systems struggle with factual accuracy, rating consistency, and analytical depth.
Approach:	They propose a framework for generating comprehensive and factually grounded scientific paper reviews using supervised fine-tuning and reinforcement learning.
Outcome:	The proposed framework outperforms existing methods on ICLR 2025 papers.

MDPO: Customized Direct Preference Optimization with a Metric-based Sampler for Question and Answer Generation (2025.coling-main)

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Yihang Wang, Bowen Tian, Yueyang Su, Yixing Fan, Jiafeng Guo

Question Answering

Challenge:	Existing methods for QA data generation are limited by the dependence of existing evaluation metrics on ground truth labels.
Approach:	They propose a set of unsupervised evaluation metrics for QA data that enable multidimensional assessment based on the relationships among context,question and answer.
Outcome:	The proposed method outperforms state-of-the-art methods on public datasets and shows that it produces high-quality and domain-specific QA pairs.

QSpell 250K: A Large-Scale, Practical Dataset for Chinese Search Query Spell Correction (2025.naacl-industry)

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Dezhi Ye, Haomei Jia, Junwei Hu, Tian Bowen, Jie Liu, Haijin Liang, Jin Ma, Wenmin Wang

Information Retrieval

Challenge:	Chinese Search Query Spell Correction is a task designed to identify and correct typographical errors within queries.
Approach:	They propose a large-scale benchmark specifically developed for Chinese Query Spell Correction.
Outcome:	The proposed benchmark covers a broad range of topics, including formal entities, everyday colloquialisms and idiomatic expressions.

Semi-Supervised Lifelong Language Learning (2022.findings-emnlp)

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Yingxiu Zhao, Yinhe Zheng, Bowen Yu, Zhiliang Tian, Dongkyu Lee, Jian Sun, Yongbin Li, Nevin L. Zhang

Low-Resource NLP

Challenge:	Existing methods to learn languages only focus on supervised learning, and unlabeled data is underexplored.
Approach:	They propose a semi-supervised lifelong language learning setting where a model learns sequentially arriving language tasks with both labeled and unlabeled data.
Outcome:	The proposed model outperforms baseline models on various language tasks and is effective and superior to existing models.

Intuitive Fine-Tuning: Towards Simplifying Alignment into a Single Process (2025.acl-long)

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Ermo Hua, Biqing Qi, Kaiyan Zhang, Kai Tian, Xingtai Lv, Ning Ding, Bowen Zhou

Language Models

Challenge:	Supervised Fine-Tuning (SFT) and Preference Optimization (PO) are key processes for aligning Language Models with human preferences post pre-training.
Approach:	They propose to combine Supervised Fine-Tuning and Preference Optimization (PO) with two sub-processes defined at token level within the Markov Decision Process (MDP)
Outcome:	The proposed process performs comparably or even superiorly to SFT and some typical PO methods across several tasks, particularly those requires generation, reasoning, and fact-following abilities.

SDAR: A Synergistic Diffusion-AutoRegression Paradigm for Scalable Sequence Generation (2026.findings-acl)

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Shuang Cheng, Yihan Bian, Dawei Liu, Yuhua Jiang, Yihao Liu, Linfeng Zhang, Qian Yao, Zhongbo Tian, Wenhai Wang, Qipeng Guo, Kai Chen, Biqing Qi, Bowen Zhou

Language Models

Challenge:	Autoregressive (AR) language models are a dominant paradigm in the field of parallelism and non-causal modeling.
Approach:	They propose a blockwise discrete diffusion model that preserves AR-compatible serving while enabling parallel intra-block generation.
Outcome:	The proposed model achieves theoretical speedups over 5 and wall-clock speedup of 2.3 on H200 GPUs in latency-critical regimes.

Best Practices for Distilling Large Language Models into BERT for Web Search Ranking (2025.coling-industry)

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Dezhi Ye, Junwei Hu, Jiabin Fan, Bowen Tian, Jie Liu, Haijin Liang, Jin Ma

Semantic Text Processing

Challenge:	Recent studies have highlighted the potential of Large Language Models (LLMs) as zero-shot relevance rankers.
Approach:	They propose to use a ranking loss to transfer ranking knowledge from LLMs to smaller models like BERT.
Outcome:	The proposed model has been successfully integrated into a commercial web search engine as of February 2024.

Rethinking Expert Trajectory Utilization in LLM Post-training for Mathematical Reasoning (2026.acl-long)

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Bowen Ding, Yuhan Chen, Jiayang Lyu, Jiyao Yuan, Qi Zhu, Shuangshuang Tian, Dantong Zhu, Futing Wang, Heyuan Deng, Fei Mi, Lifeng Shang, Tao Lin

Reasoning

Challenge:	evolving generic Large Language Models into specialized Large Reasoning Models requires effective post-training.
Approach:	They propose a plasticity-ceiling framework to harness expert trajectories . they establish the Sequential SFT-then-RL pipeline as the superior standard .
Outcome:	The proposed framework overcomes stability and premature convergence deficits in synchronized approaches.

Less Languages, Less Tokens: An Efficient Unified Logic Cross-lingual Chain-of-Thought Reasoning Framework (2026.acl-long)

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Chenyuan Zhang, Qiguang Chen, Xie Chen, Zhuotao Tian, Bowen Xing, Meishan Zhang, Libo Qin, Baotian Hu, Min Zhang

Multilinguality

Challenge:	Existing methods for cross-lingual chain-of-thought (XCoT) with self-consistency are costly due to extensive sampling of full trajectories across languages.
Approach:	They propose a cross-lingual chain-of-thought framework that minimizes redundancy in token usage and latency.
Outcome:	Experiments on polymath show that UL-XCoT reduces decoding token costs and latency by 50% . UL XCot also aggregates remaining high-quality reasoning paths via voting .