Papers by Xiaobin Chen

Agentic Knowledgeable Self-awareness (2025.acl-long)

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Shuofei Qiao, Zhisong Qiu, Baochang Ren, Xiaobin Wang, Xiangyuan Ru, Ningyu Zhang, Xiang Chen, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen

Challenge:	Large Language Models (LLMs) have achieved considerable performance across various agentic planning tasks.
Approach:	They propose a data-centric approach that applies agents with knowledgeable self-awareness like humans to a heuristic situation judgement criterion to mark special tokens on their self-explored trajectories for collecting training data.
Outcome:	The proposed paradigm outperforms baseline models on various tasks with minimal external knowledge.

SQLForge: Synthesizing Reliable and Diverse Data to Enhance Text-to-SQL Reasoning in LLMs (2025.findings-acl)

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Yu Guo, Dong Jin, Shenghao Ye, Shuangwu Chen, Jianyang Jianyang, Xiaobin Tan

Programming Languages in NLP

Challenge:	Existing closed-source LLMs have a performance gap in text-to-SQL reasoning tasks.
Approach:	They propose a SQL-based approach to synthesize reliable data to enhance text-to-SQL reasoning in LLMs.
Outcome:	The proposed model achieves state-of-the-art accuracy on the widely recognized Spider and BIRD benchmarks, significantly narrowing the performance gap with closed-source methods.

Few-shot Classification with Hypersphere Modeling of Prototypes (2023.findings-acl)

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Ning Ding, Yulin Chen, Ganqu Cui, Xiaobin Wang, Haitao Zheng, Zhiyuan Liu, Pengjun Xie

Semantic Text Processing

Challenge:	Existing methods for fewshot learning use embeddings in space, but they lack expressivity and are difficult to perform statistically.
Approach:	They propose a method where class information is represented by hyperspheres with dynamic sizes with two sets of learnable parameters: the hypersphere’s center and the radius.
Outcome:	The proposed method is much more expressive than embeddings and performs better than statistical modeling.

Exploring Lottery Prompts for Pre-trained Language Models (2023.acl-long)

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Yulin Chen, Ning Ding, Xiaobin Wang, Shengding Hu, Haitao Zheng, Zhiyuan Liu, Pengjun Xie

Language Models

Challenge:	Existing approaches to optimize pre-trained language models are expensive and slow to scale.
Approach:	They propose to search for instance-level lottery prompts and generalize them to unseen data . they validate the assumption that for every instance, there is almost always a lottery prompt that induces the correct prediction from the PLM .
Outcome:	The proposed method can achieve comparable results with other gradient-free and optimization-free baselines.

Few-NERD: A Few-shot Named Entity Recognition Dataset (2021.acl-long)

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Ning Ding, Guangwei Xu, Yulin Chen, Xiaobin Wang, Xu Han, Pengjun Xie, Haitao Zheng, Zhiyuan Liu

Low-Resource NLP

Challenge:	Existing approaches to few-shot named entity recognition (NER) focus on coarse-grained entities with few examples, while most unseen entities are fine-grounded.
Approach:	They present a human-annotated few-shot named entity recognition dataset . they construct benchmark tasks to assess the generalization capability of models .
Outcome:	The proposed model is the first few-shot NER dataset and the largest human-crafted NER data set.

Memp: Exploring Agent Procedural Memory (2026.findings-acl)

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Runnan Fang, Yuan Liang, Xiaobin Wang, Jialong Wu, Shuofei Qiao, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang

Challenge:	Large Language Models (LLMs) based agents suffer from brittle procedural memory that is manually engineered or entangled in static parameters.
Approach:	They propose a procedural-memory repository that distills past agent trajectories into fine-grained, step-by-step instructions and higher-level, script-like abstractions.
Outcome:	The proposed repository can be used to improve agents' performance on travelplanner and Alfworld.

RiT: Rubrics-in-Thinking Reinforcement Learning for Improved Reasoning in Large Language Models (2026.findings-acl)

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Xiaobin Tian, Shuai Yuan, Muyun Ding, Haonan Chen, Xiaoxi Jiang

Language Models

Challenge:	Large Reasoning Models benefit from generating intermediate reasoning steps alongside final answers.
Approach:	They propose a framework to introduce thinking-rubric supervision into intermediate reasoning.
Outcome:	The proposed framework outperforms outcome-only RL baselines on reasoning-intensive and open-ended tasks.

Rethinking Table Pruning in TableQA: From Sequential Revisions to Gold Trajectory-Supervised Parallel Search (2026.acl-long)

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Yu Guo, Shenghao Ye, Shuangwu Chen, Zijian Wen, Tao Zhang, Bai Qirui, Dong Jin, Yunpeng Hou, Huasen He, null Jianyang, Xiaobin Tan

Multimodality

Challenge:	Existing pruning methods rely on sequential revisions and unreliable critique signals . Existing methods fail to detect the loss of answer-critical data .
Approach:	They propose a table pruning framework which transforms table pruning to gold trajectory-supervised parallel search.
Outcome:	The proposed framework outperforms the strongest baseline pruning framework by 3.2% on various tabular reasoning tasks.

Prompt-learning for Fine-grained Entity Typing (2022.findings-emnlp)

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Ning Ding, Yulin Chen, Xu Han, Guangwei Xu, Xiaobin Wang, Pengjun Xie, Haitao Zheng, Zhiyuan Liu, Juanzi Li, Hong-Gee Kim

Language Models

Challenge:	Extensive experiments on fine-grained entity typing under fully supervised, few-shot, and zero-shot settings show the effectiveness of prompt-learning.
Approach:	They propose a prompt-learning pipeline that stimulates versatile knowledge of pre-trained language models (PLMs) by constructing entity-oriented verbalizers and templates and conducting masked language modeling.
Outcome:	The proposed approach can be applied to fine-grained entity typing in fully supervised, few-shot, and zero-shot scenarios.

SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement (2025.acl-short)

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Runnan Fang, Xiaobin Wang, Yuan Liang, Shuofei Qiao, Jialong Wu, Zekun Xi, Ningyu Zhang, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen

Challenge:	Using Large Language Models (LLMs)-based agents can enhance their understanding of environments and tasks.
Approach:	They propose a framework that allows agents to synthesize possible scenarios with multi-step action invocation within the action space and perform Monte Carlo Tree Search exploration to refine their action knowledge in the current environment.
Outcome:	The proposed framework synthesizes possible scenarios with multi-step action invocation within the action space and performs Monte Carlo Tree Search exploration to refine action knowledge in the current environment.

CTAP for Chinese:A Linguistic Complexity Feature Automatic Calculation Platform (2022.lrec-1)

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Yue Cui, Junhui Zhu, Liner Yang, Xuezhi Fang, Xiaobin Chen, Yujie Wang, Erhong Yang

Semantic Text Processing

Challenge:	Existing tools to analyze linguistic complexity are limited and different because of different research purposes.
Approach:	They propose to integrate Chinese component into CTAP to analyze linguistic complexity . they propose to use 196 linguistic complex indexes to calculate linguistic characteristics .
Outcome:	The proposed indexes are compared with three linguistic complexity tools for Chinese . the proposed index sets include four levels of 196 linguistic complex indexe .