Papers by Jian Guan
From the Least to the Most: Building a Plug-and-Play Visual Reasoner via Data Synthesis (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing models consisting of multiple steps of visual and language processing are limited in the visual and visual processing community . a visual reasoner is a plug-and-play approach that can be used to improve VLMs' reasoning abilities. |
| Approach: | They propose a least-to-most visual reasoning paradigm that divides a question into sub-questions and invokes external tools for resolving sub-questions. |
| Outcome: | The proposed method can improve four VLMs on four VQA benchmarks. |
Beyond Online Sampling: Bridging Offline-to-Online Alignment via Dynamic Data Transformation for LLMs (2025.emnlp-main)
Copied to clipboard
| Challenge: | Direct Preference Optimization (DPO) eliminates complex reward modeling in aligning large language models with human preferences, but its online variant faces significant efficiency bottlenecks due to costly real-time preference sampling and the reward model annotation. |
| Approach: | They propose a framework that transforms static datasets into dynamically adaptive equivalents without the need for an explicit reward model. |
| Outcome: | The proposed approach matches or exceeds the performance of a fully online DPO. |
LOT: A Story-Centric Benchmark for Evaluating Chinese Long Text Understanding and Generation (2022.tacl-1)
Copied to clipboard
| Challenge: | Existing benchmarks for natural language processing focus on understanding or generating short texts . lack of standardized benchmarks makes it difficult to assess and compare models . |
| Approach: | They propose a story-centric benchmark for Chinese long text modeling that aggregates two understanding tasks and two generation tasks. |
| Outcome: | The proposed model outperforms similar-sized models on understanding and generation tasks. |
FlexQuant: A Flexible and Efficient Dynamic Precision Switching Framework for LLM Quantization (2025.findings-emnlp)
Copied to clipboard
Fangxin Liu, Zongwu Wang, Jinhong Xia, Junping Zhao, Shouren Zhao, Jinjin Li, Jian Liu, Li Jiang, Haibing Guan
| Challenge: | Existing methods for quantization of large language models struggle to adapt to dynamic workloads. |
| Approach: | a new framework optimizes the trade-off between inference speed and accuracy . FlexQuant enables fine-grained, layer-wise mixed-precision quantization . |
| Outcome: | a new framework optimizes the trade-off between inference speed and accuracy . it achieves a 1.3 speedup across diverse language tasks with negligible accuracy loss . |
Automatic Comment Generation for Chinese Student Narrative Essays (2022.emnlp-demos)
Copied to clipboard
| Challenge: | Existing studies focus on giving discrete scores for holistic quality or distinct traits, but real-world teachers usually provide detailed comments in natural language, which are more informative than single scores. |
| Approach: | They propose a model which generates comments for specified segments from given student narrative essays using a human-written Chinese dataset. |
| Outcome: | The proposed model outperforms baselines and has 91% success rate. |
Generating Informative Responses with Controlled Sentence Function (P18-1)
Copied to clipboard
| Challenge: | Sentence function is a significant factor to achieve the purpose of the speaker, but has not been touched in large-scale conversation generation. |
| Approach: | They propose a model to generate informative responses with controlled sentence function using a latent variable and a type controller to deal with compatibility. |
| Outcome: | The proposed model outperforms state-of-the-art models and generates responses with controlled sentence function and informative content. |
Mixture-of-Modules: Reinventing Transformers as Dynamic Assemblies of Modules (2024.emnlp-main)
Copied to clipboard
| Challenge: | Empirical results show that MoMs consistently outperform vanilla transformers . |
| Approach: | They propose an architecture that allows for a mixture-of-modules computation that uses a finite set of modules defined by multi-head attention and feed-forward networks. |
| Outcome: | The proposed architecture outperforms vanilla Transformers and their variants in multiple ways. |
UNION: An Unreferenced Metric for Evaluating Open-ended Story Generation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing metrics for open-ended text generation are poorly correlated with human judgments . despite the success of existing metrics, there are few plausible outputs for the same input . |
| Approach: | They propose a UNreferenced measure for evaluating open-ended story generation . it is built on top of BERT and is trained to distinguish human-written stories from negative samples . |
| Outcome: | The proposed measure is more generalizable than state-of-the-art metrics and correlates better with human judgments. |
Multi-Stage LLM Fine-Tuning with a Continual Learning Setting (2025.findings-naacl)
Copied to clipboard
Changhao Guan, Chao Huang, Hongliang Li, You Li, Ning Cheng, Zihe Liu, Yufeng Chen, Jinan Xu, Jian Liu
| Challenge: | Large language models (LLMs) have made significant progress in knowledge-intensive applications, but they may face a multi-stage continuous learning scenario. |
| Approach: | They propose a multi-stage continuous learning paradigm that includes a preference-based learning bias to identify potential knowledge conflicts and a self-distillation-based data augmentation strategy to expand and enrich the training corpus. |
| Outcome: | The proposed learning paradigm achieves a significant improvement in accuracy after 7 stages of fine-tuning compared to previous methods while preserving general knowledge. |
A Survey on Personalized Alignment—The Missing Piece for Large Language Models in Real-World Applications (2025.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their transition to real-world applications reveals a critical limitation: the inability to adapt to individual preferences while maintaining alignment with universal human values. |
| Approach: | They propose a framework that enables LLMs to adapt their behavior within ethical boundaries based on individual preferences. |
| Outcome: | The proposed framework analyzes implementation approaches and evaluates their effectiveness across various scenarios. |
XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser (2025.coling-main)
Copied to clipboard
Xianfu Cheng, Hang Zhang, Jian Yang, Xiang Li, Weixiao Zhou, Fei Liu, Kui Wu, Xiangyuan Guan, Tao Sun, Xianjie Wu, Tongliang Li, Zhoujun Li
| Challenge: | Document AI parsing semi-structured image form is a key information extraction task. |
| Approach: | They propose a multimodal and multilingual semi-structured FORM PARSER which integrates SER and relation extraction into a unified framework. |
| Outcome: | The proposed framework achieves up to 1.79% improvement on RE tasks in multilingual and zero-shot settings. |
Self-Supervised Sentence Polishing by Adding Engaging Modifiers (2023.acl-demo)
Copied to clipboard
| 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. |
Language Models Hallucinate, but May Excel at Fact Verification (2024.naacl-long)
Copied to clipboard
| Challenge: | Recent advances in large language models (LLMs) have produced non-factual outputs . however, current LLMs suffer from the hallucination issue . |
| Approach: | They propose to use instruction-tuned LLMs to generate factual outputs . they find that FLAN-T5-11B performs best as a fact verifier . |
| Outcome: | The proposed method outperforms more capable LLMs like GPT3.5 and ChatGPT in the human evaluation. |
Re3Dial: Retrieve, Reorganize and Rescale Conversations for Long-Turn Open-Domain Dialogue Pre-training (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing pre-training models lack long-turn dialogue sessions due to the scarcity of long-term sessions. |
| Approach: | They propose a framework that can automatically construct billion-scale long-turn dialogues by reorganizing existing short-turn ones. |
| Outcome: | The proposed framework can automatically construct billion-scale long-turn dialogues by reorganizing existing short-turn ones. |
Novel Slot Detection With an Incremental Setting (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Current dialogue systems face diverse user requests and rapid change domains, making quickly adapt to scenarios with previous unseen slot types becomes a major challenge. |
| Approach: | They propose an incremental novel slot detection task which separates the dialogue system to deal with novel types as two major phrases: 1) model discovers unknown slots; 2) training model to possess the capability to handle new classes. |
| Outcome: | The proposed approach overcomes catastrophic forgetting during the process of INSD and is highly effective. |
A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation (2020.tacl-1)
Copied to clipboard
| Challenge: | Existing models for story generation suffer from repetition, logic conflicts, and lack of long-range coherence . |
| Approach: | They propose to utilize commonsense knowledge from external knowledge bases to generate reasonable stories by multi-task learning. |
| Outcome: | The proposed model can generate more reasonable stories than state-of-the-art models, compared with existing models, showing that it can capture useful semantic and syntactic features. |
A Corpus for Understanding and Generating Moral Stories (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing tasks for evaluating story understanding and generation focus on reasoning plots from context, but they focus on bridging plots with implied morals. |
| Approach: | They propose two understanding tasks and two generation tasks to assess machines' ability to bridge story plots and implied morals. |
| Outcome: | The proposed tasks are based on a dataset of Chinese and English moral stories . they show that the proposed models can perform better than existing models . |
M3TQA: Massively Multilingual Multitask Table Question Answering (2026.findings-acl)
Copied to clipboard
Daixin Shu, Jian Yang, Zhenhe Wu, Xianjie Wu, Xianfu Cheng, Guan Xiangyuan, Yanghai Wang, Pengfei Wu, Tingyang Yang, Hualei Zhu, Wei Zhang, Ge Zhang, Jiaheng Liu, Zhoujun Li
| Challenge: | Existing multilingual table benchmarks suffer from geolinguistic imbalance - overrepresenting certain languages and lacking sufficient scale for rigorous cross-lingual analysis. |
| Approach: | They propose a framework for massively multilingual table question answering that includes tables expanded to 97 languages from Chinese and English sources. |
| Outcome: | Experiments on state-of-the-art LLMs show that synthetically generated training data significantly boosts performance, especially for low-resource languages. |
Stylized Story Generation with Style-Guided Planning (2021.findings-acl)
Copied to clipboard
| Challenge: | Current storytelling systems focus more on generating stories with coherent plots regardless of the narration style. |
| Approach: | They propose a novel task, stylized story generation, that first plans stylized keywords and then generates the whole story with the guidance of the keywords. |
| Outcome: | The proposed model can generate emotion-driven or event-driven stories based on the ROCStories dataset . |
PromptCoT: Synthesizing Olympiad-level Problems for Mathematical Reasoning in Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | PromptCoT is a novel approach for synthesizing Olympiad-level math problems . it integrates rationale generation and mathematical concepts to generate complex problems based on concepts and rationale behind problem construction. |
| Approach: | They propose a method for automatically generating high-quality Olympiad-level math problems . they use mathematical concepts and the rationale behind problem construction to synthesize complex problems based on mathematical concepts . |
| Outcome: | The proposed method outperforms existing problem generation methods on benchmarks including GSM8K, MATH-500, and AIME2024. |
Mitigating the Learning Bias towards Repetition by Self-Contrastive Training for Open-Ended Generation (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing language models generate repetitive texts with greedy decoding or beam search. |
| Approach: | They propose a self-contrastive training technique to penalize the output of a premature checkpoint of the same model when it incorrectly predicts repetition. |
| Outcome: | The proposed training mitigates repetition while maintaining fluency while minimizing the overestimation of token-level repetition probabilities. |
Persona-Guided Planning for Controlling the Protagonist’s Persona in Story Generation (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing methods to control the protagonist's persona in story generation are implicitly and sparsely embodied in stories, so we propose a planning-based generation model called ConPer to explicitly model the relationship between personas and events. |
| Approach: | They propose a model to control the protagonist's persona in story generation by predicting one target sentence and planning the plot as a sequence of keywords with the guidance of the predicted persona-related events and commonsense knowledge. |
| Outcome: | The proposed model outperforms state-of-the-art models for generating more coherent and persona-controllable stories. |
OAgents: An Empirical Study of Building Effective Agents (2025.findings-emnlp)
Copied to clipboard
He Zhu, Tianrui Qin, King Zhu, Heyuan Huang, Yeyi Guan, Jinxiang Xia, Hanhao Li, Yi Yao, Ningning Wang, Pai Liu, Tianhao Peng, Xin Gui, Li Xiaowan, Yuhui Liu, Xiangru Tang, Jian Yang, Ge Zhang, Xitong Gao, Yuchen Eleanor Jiang, Changwang Zhang, Jun Wang, Jiaheng Liu, Wangchunshu Zhou
| Challenge: | a recent study shows that agent research practices are far from standard, rigorous . lack of a standard evaluation protocol makes previous works not reproducible, authors say . |
| Approach: | They conduct an empirical study on the GAIA benchmark to investigate agent design choices . they find that lack of a standard evaluation protocol makes previous works not reproducible . |
| Outcome: | The proposed framework achieves state-of-the-art performance among open-source projects. |
OpenMEVA: A Benchmark for Evaluating Open-ended Story Generation Metrics (2021.acl-long)
Copied to clipboard
Jian Guan, Zhexin Zhang, Zhuoer Feng, Zitao Liu, Wenbiao Ding, Xiaoxi Mao, Changjie Fan, Minlie Huang
| Challenge: | Existing automatic metrics are observed to correlate poorly with human evaluation. |
| Approach: | They propose to use OpenMEVA to evaluate open-ended story generation metrics. |
| Outcome: | The proposed test suite assesses the capabilities of open-ended story generation metrics on annotated stories and auto-constructed test examples. |
StoryTrans: Non-Parallel Story Author-Style Transfer with Discourse Representations and Content Enhancing (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies on text style transfer neglect long style transfer at the discourse level. |
| Approach: | They propose a model that transfers text style into target styles with learnable style embeddings . they use a mask-and-fill framework to explicitly fuse style-specific keywords into generation . |
| Outcome: | The proposed model outperforms baselines in style transfer and content preservation. |
From 1,000,000 Users to Every User: Scaling Up Personalized Preference for User-level Alignment (2026.acl-long)
Copied to clipboard
| Challenge: | Current approaches to align large language models assume uniform human preferences, overlooking the diversity inherent in human populations. |
| Approach: | They propose a framework for scalable personalized alignment of large language models . they establish a preference space characterizing psychological and behavioral dimensions . |
| Outcome: | The proposed framework improves on existing methods with an average of 17.06% accuracy gain across four benchmarks and a strong adaptation capability to novel preferences. |
Long Text Generation by Modeling Sentence-Level and Discourse-Level Coherence (2021.acl-long)
Copied to clipboard
| Challenge: | Existing generation models struggle to maintain a coherent event sequence throughout the generated text. |
| Approach: | They propose a long text generation model which can represent prefix sentences at sentence level and discourse level in the decoding process. |
| Outcome: | The proposed model can generate more coherent texts than state-of-the-art models. |