Papers by Xinyi Hu
RoadMapper: A Multi-Agent System for Roadmap Generation of Solving Complex Research Problems (2026.findings-acl)
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Jiacheng Liu, Zichen Tang, Zhongjun Yang, Xinyi Hu, Xueyuan Lin, Linwei Jia, Ruofei Bai, Rongjin Li, Shiyao Peng, Haocheng Gao, Haihong E
| Challenge: | Existing tools to generate structured content for research tasks are limited in their ability to generate high-quality roadmaps. |
| Approach: | They propose a benchmark to evaluate the ability of large language models (LLMs) to generate high-quality roadmaps for solving complex research problems. |
| Outcome: | The proposed system can improve LLMs’ ability for roadmap generation while saving 84% of the time required by human experts. |
ChatMap: Mining Human Thought Processes for Customer Service Chatbots via Multi-Agent Collaboration (2025.findings-acl)
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Xinyi Jiang, Tianyi Hu, Yuheng Qin, Guoming Wang, Zhou Huan, Kehan Chen, Gang Huang, Rongxing Lu, Siliang Tang
| Challenge: | Existing methods for enhancing dialogue performance rely on summarizing behavior . e-commerce chatbots need to align their dialogue strategies with human behavior to achieve coherent, human-like conversations with customers. |
| Approach: | They propose a method to extract core patterns from dialogue data and integrate them into models by mining service thought processes using a multi-agent aPproach. |
| Outcome: | The proposed method outperforms manual methods and outperfies baselines on Taobao in China. |
Uncovering Scaling Laws for Large Language Models via Inverse Problems (2025.findings-emnlp)
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Arun Verma, Zhaoxuan Wu, Zijian Zhou, Xiaoqiang Lin, Zhiliang Chen, Rachael Hwee Ling Sim, Rui Qiao, Jingtan Wang, Nhung Bui, Xinyuan Niu, Wenyang Hu, Gregory Kang Ruey Lau, Zi-Yu Khoo, Zitong Zhao, Xinyi Xu, Apivich Hemachandra, See-Kiong Ng, Bryan Kian Hsiang Low
| Challenge: | Large Language Models (LLMs) have achieved remarkable success across diverse domains. |
| Approach: | inverse problems can efficiently uncover scaling laws that guide the building of LLMs, authors argue . authors propose brute-force approaches to improve LLM training costs due to high costs . |
| Outcome: | This paper advocates that inverse problems can efficiently uncover scaling laws that guide the building of LLMs to achieve the desirable performance with significantly better cost-effectiveness. |
2INER: Instructive and In-Context Learning on Few-Shot Named Entity Recognition (2023.findings-emnlp)
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| Challenge: | Named Entity Recognition (NER) tasks are a fundamental task of natural language processing (NLP). |
| Approach: | They propose a text-to-text framework for Few-Shot Named Entity Recognition (NER) that employs instruction finetuning and auxiliary tasks to enhance the model's understanding of entity types in the overall semantic context of a sentence. |
| Outcome: | The proposed framework outperforms existing Few-Shot NER methods and remains competitive with state-of-the-art NER algorithms. |
MTP-RL: Acceleration of Reinforcement Learning Rollouts with Policy-Aligned Multi-Token Prediction (2026.findings-acl)
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| Challenge: | Reinforcement learning (RL) is widely applied to boost the performance of pretrained models, yet its training efficiency is severely constrained by rollout generation. |
| Approach: | They propose a framework that accelerates the rollout phase for diverse models by equipping a pipeline to equip the multi-layer parameter-sharing MTP for all models and an advantage-aware MTP optimization strategy. |
| Outcome: | The proposed framework achieves stable growth of acceptance length during RL training, and also accelerates RL rollouts, achieving an average 23.1%–55.3% reduction in rollout time compared to baselines. |
Continual Event Extraction with Semantic Confusion Rectification (2023.emnlp-main)
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| Challenge: | Existing studies focus on continual event extraction to extract incessantly emerging information . the semantic confusion on event types stems from the annotations of the same text being updated over time . |
| Approach: | They propose a continual event extraction model with semantic confusion rectification to reduce semantic confusion. |
| Outcome: | The proposed model outperforms state-of-the-art models and is proficient in imbalanced datasets. |
ControlText: Unlocking Controllable Fonts in Multilingual Text Rendering without Font Annotations (2025.findings-emnlp)
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Bowen Jiang, Yuan Yuan, Xinyi Bai, Zhuoqun Hao, Alyson Yin, Yaojie Hu, Wenyu Liao, Lyle Ungar, Camillo Jose Taylor
| Challenge: | a new method for visual text rendering requires glyph annotations to be obtained . |
| Approach: | They propose a model that integrates diffusion with a text segmentation model to achieve multilingual text rendering using just raw images without font label annotations. |
| Outcome: | The proposed model can achieve font-controllable multilingual text rendering without label annotations. |
EcoLANG: Efficient and Effective Agent Communication Language Induction for Social Simulation (2025.findings-emnlp)
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| Challenge: | Large language models (LLMs) have demonstrated impressive ability to role-play humans and replicate complex social dynamics. |
| Approach: | They propose an efficient agent communication language induction for social simulations that reduces token consumption by over 20%. |
| Outcome: | The proposed model reduces token consumption by over 20% while preserving human language. |
Rethinking Reasoning: A Survey on Reasoning-based Backdoors in LLMs (2026.findings-acl)
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| Challenge: | Recent models such as OpenAI o1 and DeepSeek-R1 produce explicit reasoning traces, often via Chain-of-Thought prompting. |
| Approach: | They propose a taxonomy that offers a unified perspective for summarizing existing approaches and categorizing reasoning-based backdoor attacks into associative, passive, and active. |
| Outcome: | The proposed taxonomy categorizes reasoning-based backdoor attacks into associative, passive, and active. |
Uni-Retrieval: A Multi-Style Retrieval Framework for STEM’s Education (2025.acl-long)
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| Challenge: | Current retrieval models focus on natural text-image retrieval, which is insufficient for STEM education contexts due to ambiguities in the retrieval process. |
| Approach: | They propose a diverse expression retrieval task tailored to educational scenarios . they extract query style features as prototypes and build a continuously updated Prompt Bank . |
| Outcome: | The proposed model outperforms existing retrieval models in most retrieval tasks. |
Domain Differential Adaptation for Neural Machine Translation (D19-56)
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| Challenge: | Neural networks are data hungry and domain sensitive, so it is difficult to obtain labeled data for every domain. |
| Approach: | They propose a framework for domain adaptation where we model the difference between domains instead of smoothing over them. |
| Outcome: | The proposed framework improves on domain adaptation in multiple experimental settings. |
compare-mt: A Tool for Holistic Comparison of Language Generation Systems (N19-4)
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| Challenge: | Unlike machine translation, natural language outputs are nuanced and there are no clear yes/no distinctions about whether they are correct or not. |
| Approach: | They describe compare-mt, a tool for holistic analysis and comparison of the results of systems for language generation tasks such as machine translation. |
| Outcome: | The compare-mt tool is an open-source pure-python package that has already proven useful to generate analyses that have been used in our papers. |
Learning Mutually Informed Representations for Characters and Subwords (2024.findings-naacl)
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| Challenge: | Pretrained language models rely on subword tokenization to process text as a sequence of subwords. |
| Approach: | They propose a character-subword language model that integrates character and subword modalities into one model. |
| Outcome: | The proposed model outperforms its backbone language models on English sequence labeling and classification tasks. |
Extract-Select: A Span Selection Framework for Nested Named Entity Recognition with Generative Adversarial Training (2022.findings-acl)
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| Challenge: | Existing studies treat named entity recognition as a sequential labeling problem. |
| Approach: | They propose a span selection framework for nested named entity recognition . they propose nesting entities with different input categories would be separately extracted . |
| Outcome: | The proposed framework outperforms competing models on four benchmark datasets. |
Mixture of LoRA Experts for Continual Information Extraction with LLMs (2025.findings-emnlp)
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| Challenge: | Existing methods to continual information extraction are either task-specialized for a single task or suffer from catastrophic forgetting and insufficient knowledge transfer in continual IE. |
| Approach: | They propose a continual IE model that uses token-level mixture of LoRA experts with LLMs to extract emerging information across diverse IE tasks incessantly while avoiding forgetting. |
| Outcome: | The proposed model achieves state-of-the-art performance, effectively mitigating catastrophic forgetting and enhancing knowledge transfer in continual IE. |
SaCa: A Highly Compatible Reinforcing Framework for Knowledge Graph Embedding via Structural Pattern Contrast (2025.findings-emnlp)
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| Challenge: | Existing knowledge Graph Embedding approaches lack structural semantics of knowledge graphs . structure-aware calibration (SaCa) is a framework designed to calibrate KGEs based on global structural patterns. |
| Approach: | a new framework is designed to calibrate knowledge graphs using global structural patterns. |
| Outcome: | a new framework can calibrate KGE models using global structural patterns . the framework consistently boosts performance across ten models on link prediction and entity classification tasks . |
LRBench and Judge-R1: Principled Evaluation and Training of LLM-Based Judges for Long-Context Reasoning (2026.findings-acl)
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| Challenge: | Existing benchmarks for evaluating large language models (LLMs) under long contexts are underexplored. |
| Approach: | They propose a large-scale benchmark for evaluating large language models (LLMs) that combines reinforcement learning with multi-turn search to enable grounded and principle-aware evaluation. |
| Outcome: | The proposed model outperforms single-turn baselines across domains and principles. |
Serial Contrastive Knowledge Distillation for Continual Few-shot Relation Extraction (2023.findings-acl)
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| Challenge: | Existing models for few-shot relation extraction (RE) are not suitable for continual few-sshot RE. |
| Approach: | They propose a new model to train a model for new relations with few labeled training data. |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets. |
Mitigating Shortcut Learning via Smart Data Augmentation based on Large Language Model (2025.coling-main)
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| Challenge: | Existing methods to improve shortcut learning performance are limited by manual definition of shortcuts and inherent confirmation bias during model training. |
| Approach: | They propose a method of Smart Data Augmentation based on Large Language Models to identify shortcuts and generate their anti-shortcut counterparts. |
| Outcome: | The proposed method shows an improvement of 5.61% across various natural language processing tasks. |
LEDOM: Reverse Language Model (2026.acl-long)
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Xunjian Yin, Sitao Cheng, Yuxi Xie, Xinyu Hu, Li Lin, Xinyi Wang, Liangming Pan, William Yang Wang, Xiaojun Wan
| Challenge: | Autoregressive language models are trained exclusively left-to-right, yet they are limited in their ability to factorize text. |
| Approach: | They propose a purely reverse autoregressive language model that factorizes text as a product of left-to-right conditionals. |
| Outcome: | The proposed model can be used to score forward outputs using reverse posterior estimates. |
Position Paper: Data-Centric AI in the Age of Large Language Models (2024.findings-emnlp)
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Xinyi Xu, Zhaoxuan Wu, Rui Qiao, Arun Verma, Yao Shu, Jingtan Wang, Xinyuan Niu, Zhenfeng He, Jiangwei Chen, Zijian Zhou, Gregory Kang Ruey Lau, Hieu Dao, Lucas Agussurja, Rachael Hwee Ling Sim, Xiaoqiang Lin, Wenyang Hu, Zhongxiang Dai, Pang Wei Koh, Bryan Kian Hsiang Low
| Challenge: | a paper proposes a data-centric perspective of AI research, focusing on large language models. |
| Approach: | They propose a data-centric viewpoint of AI research, focusing on large language models . they propose four scenarios centered around data, including data curation, attribution, knowledge transfer . |
| Outcome: | The proposed research focuses on large language models with data centric benchmarks . the proposed benchmarks can be used to develop new data curation methods . |