Papers by Huang Siyuan
Evolving Beyond Snapshots: Harmonizing Structure and Sequence via Entity State Tuning for Temporal Knowledge Graph Forecasting (2026.acl-long)
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| Challenge: | Temporal knowledge graphs (TKGs) require predicting future facts by modeling structural dependencies within each snapshot and temporal evolution across snapshots. |
| Approach: | They propose an encoder-agnostic framework that provides persistent entity states . EST maintains a global state buffer and aligns structural evidence with sequential signals . |
| Outcome: | Experiments show that EST improves diverse backbones and achieves state-of-the-art performance. |
HAF-RM: A Hybrid Alignment Framework for Reward Model Training (2025.acl-long)
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Shujun Liu, Xiaoyu Shen, Yuhang Lai, Siyuan Wang, Shengbin Yue, Zengfeng Huang, Xuanjing Huang, Zhongyu Wei
| Challenge: | Recent studies have focused on enhancing reward models through data improvements, following the conventional training framework for reward models that directly optimizes the predicted rewards. |
| Approach: | They propose a hybrid alignment framework **HAF-RM** that incorporates additional constraint on token-level policy probabilities in addition to the reward score. |
| Outcome: | The proposed framework can supervise the internal preference model at the token level and optimize the mapping layer of the reward model at sequence level. |
SciPedia: Unlocking the Value of Scientific Data for Pre-training (2026.acl-long)
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| Challenge: | High-quality scientific data is critical for advancing LLMs, yet academic literature remains underutilized. |
| Approach: | They construct a large-scale raw scientific corpus but identify a critical Learnability Gap . they develop a multi-stage pipeline featuring content cleaning and pedagogical augmentation . |
| Outcome: | The proposed approach boosts average performance by +2.12 (3B) and +2.95 (7B) on in-domain tasks. |
ALaRM: Align Language Models via Hierarchical Rewards Modeling (2024.findings-acl)
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| Challenge: | Current alignment approaches struggle with inconsistency and sparsity of human supervision signals. |
| Approach: | They propose a framework modeling hierarchical rewards in reinforcement learning from human feedback (RLHF) it integrates holistic rewards with aspect-specific rewards to enhance alignment of large language models with human preferences. |
| Outcome: | The proposed framework improves the alignment of large language models with human preferences by integrating holistic rewards with aspect-specific rewards. |
1+1>2: Can Large Language Models Serve as Cross-Lingual Knowledge Aggregators? (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have been recognized for their impressive capabilities in natural language processing (NLP). |
| Approach: | They propose a method to enhance the multilingual performance of Large Language Models by aggregating knowledge from diverse languages. |
| Outcome: | The proposed method reduces the performance disparity across languages and offers valuable insights for further exploration. |
Mirror-Consistency: Harnessing Inconsistency in Majority Voting (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are a widely-used decoding strategy that relies on the plurality voting rule, which focuses on the most frequent answer while overlooking all other minority responses. |
| Approach: | They propose to incorporate a ‘reflective mirror’ into the self-ensemble decoding process and enables LLMs to critically examine inconsistencies among multiple generations. |
| Outcome: | The proposed method incorporates a ‘reflective mirror’ into the self-ensemble decoding process and enables LLMs to critically examine inconsistencies among multiple generations. |
Negative Sample is Negative in Its Own Way: Tailoring Negative Sentences for Image-Text Retrieval (2022.findings-naacl)
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| Challenge: | Existing approaches to retrieve hard negative sentences are limited in the scale of the dataset thus fail to identify negative samples of high difficulty for every image. |
| Approach: | They propose to use a model to generate synthetic negative sentences with higher difficulty by masking and refilling the images and performing word discrimination and word correction tasks to improve retrieval and generation. |
| Outcome: | The proposed model generates synthetic negative sentences with higher difficulty on MS-COCO and Flickr30K and is robust and faithful to state-of-the-art training. |
Query Structure Modeling for Inductive Logical Reasoning Over Knowledge Graphs (2023.acl-long)
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| Challenge: | Existing methods for inductive reasoning over knowledge graphs lack the ability to model the logical structures of complex queries. |
| Approach: | They propose a structure-modeled textual encoding framework for inductive logical reasoning over KGs that encodes linearized query structures and entities using pre-trained language models to find answers. |
| Outcome: | The proposed framework encodes query structures and entities using pre-trained language models to find answers. |
LayoutPointer: A Spatial-Context Adaptive Pointer Network for Visual Information Extraction (2024.naacl-long)
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| Challenge: | Existing models inadequately utilize spatial information of entities, causing incorrectly linking spatially distant entities. |
| Approach: | They propose a Spatial-Context Adaptive Pointer Network to restore semantic order among entities . they propose XFUND-based tail-to-head pointer to restore the semantic order . |
| Outcome: | The proposed method outperforms existing state-of-the-art methods in F1 scores for RE tasks. |
Gumbel Reranking: Differentiable End-to-End Reranker Optimization (2025.acl-long)
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Siyuan Huang, Zhiyuan Ma, Jintao Du, Changhua Meng, Weiqiang Wang, Jingwen Leng, Minyi Guo, Zhouhan Lin
| Challenge: | Existing distillation-based approaches suffer from training-inference misalignment and fail to capture interdependencies among candidate documents. |
| Approach: | They propose a method to optimize rerankers by learning a stochastic, document-wise Top-k attention mask using the Gumbel Trick and Relaxed Top-K Sampling. |
| Outcome: | The proposed framework minimizes the overall language loss and improves recall on hotpotQA. |
PathQG: Neural Question Generation from Facts (2020.emnlp-main)
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| Challenge: | Existing research for question generation encodes text as a sequence of tokens without explicitly modeling fact information. |
| Approach: | They propose to incorporate facts in the input text for question generation in a comprehensive way. |
| Outcome: | The proposed model outperforms state-of-the-art models and human evaluation shows it generates relevant and informative questions. |
Multi-Objective Forward Reasoning and Multi-Reward Backward Refinement for Product Review Summarization (2024.lrec-main)
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| Challenge: | Product review summarization aims to generate a concise summary based on product reviews . factual accuracy, aspect comprehensiveness, and content relevance are challenges . |
| Approach: | They propose an FB-Thinker framework to improve product review summarization ability . they propose two Chinese product review summary datasets for instruction-tuning and evaluation . |
| Outcome: | The proposed framework improves product review summarization with forward reasoning and backward refinement. |
Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluation (2025.coling-main)
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| Challenge: | Recent advances in Large Language Models have demonstrated remarkable performance across tasks. |
| Approach: | They propose a benchmark self-evolving framework to dynamically evaluate rapidly advancing Large Language Models. |
| Outcome: | The proposed framework extends existing benchmarks to extend models across tasks and tasks. |
Attribution-Based Analysis and Optimization of Modular Agentic Workflows (2026.findings-acl)
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Yingxuan Yang, Bo Huang, Siyuan Qi, Chao Feng, Haoyi Hu, Yuxuan Zhu, Jinbo Hu, Haoran Zhao, Ziyi He, Xiao Liu, ZongYu Wang, Muning Wen, Lin Qiu, Xuezhi Cao, Xunliang Cai, Yong Yu, Weinan Zhang
| Challenge: | Large Language Models (LLMs) have driven the rise of agentic workflows . yet, how can we attribute performance gains to individual upgrades and their interactions? |
| Approach: | They propose a game-theoretic framework that models component upgrades as players and evaluates component coalitions to compute Shapley values. |
| Outcome: | The proposed framework provides interaction-aware attribution and recommendation for model allocation under a fixed workflow structure. |
Multi-Agent Simulator Drives Language Models for Legal Intensive Interaction (2025.findings-naacl)
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ShengbinYue ShengbinYue, Ting Huang, Zheng Jia, Siyuan Wang, Shujun Liu, Yun Song, Xuanjing Huang, Zhongyu Wei
| Challenge: | Large Language Models (LLMs) have advanced legal intelligence, but the scarcity of scenario data impedes the progress toward interactive legal scenarios. |
| Approach: | They propose a Multi-agent Legal Simulation Driver to generate synthetic data by simulating interactive legal scenarios. |
| Outcome: | The proposed framework ensures consistency of legal attributes between participants and introduces a supervisory mechanism to align participants’ characters and behaviors as well as addressing distractions. |
Mask Attention Networks: Rethinking and Strengthen Transformer (2021.naacl-main)
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Zhihao Fan, Yeyun Gong, Dayiheng Liu, Zhongyu Wei, Siyuan Wang, Jian Jiao, Nan Duan, Ruofei Zhang, Xuanjing Huang
| Challenge: | Existing research explores to enhance the two sublayers separately to improve the capability of Transformer for text representation. |
| Approach: | They propose to combine SAN and Feed-Forward Networks to create a dynamic mask attention network with a learnable mask matrix which can model localness adaptively. |
| Outcome: | The proposed model outperforms the original Transformer on translation and text summarization tasks. |
Training LLMs to be Better Text Embedders through Bidirectional Reconstruction (2025.emnlp-main)
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Chang Su, Dengliang Shi, Siyuan Huang, Jintao Du, Changhua Meng, Yu Cheng, Weiqiang Wang, Zhouhan Lin
| Challenge: | Existing text embedding approaches often leverage the embeddment of the final token, typically a reserved special token such as ‘[EOS]‘. |
| Approach: | They propose to add a new training stage before contrastive learning to enrich the semantics of the final token embedding. |
| Outcome: | The proposed training stage improves performance on the Massive Text Embedding Benchmark (MTEB), achieving new state-of-the-art results across different LLM base models and scales. |
Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning (2021.acl-long)
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| Challenge: | Existing methods for solving geometric problems are either small in scale or not publicly available. |
| Approach: | They propose a large-scale benchmark for geometric problem solving using formal language and symbolic reasoning. |
| Outcome: | The proposed approach parses geometry problems into formal language and performs symbolic reasoning step by step. |
Guardian-as-an-Advisor: Advancing Next-Generation Guardian Models for Trustworthy LLMs (2026.findings-acl)
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Yue Huang, Haomin Zhuang, Jiayi Ye, Han Bao, Yanbo Wang, Hang Hua, Siyuan Wu, Pin-Yu Chen, Xiangliang Zhang
| Challenge: | prevailing taxonomies neglect robustness and honesty, yielding safer-on-paper but less useful systems. |
| Approach: | They propose a soft-gating pipeline where a guardian predicts a binary risk label plus a concise explanation and prepends this advice to the original query for re-inference. |
| Outcome: | The proposed model maintains safety while reducing over-refusal. |
A Structure-Aware Argument Encoder for Literature Discourse Analysis (2022.coling-1)
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Yinzi Li, Wei Chen, Zhongyu Wei, Yujun Huang, Chujun Wang, Siyuan Wang, Qi Zhang, Xuanjing Huang, Libo Wu
| Challenge: | Existing research for argument representation learning treats tokens in sentences equally and ignores the implied structure information of argumentative context. |
| Approach: | They propose to separate tokens into two groups to capture structural information of arguments and to incorporate paragraph-level position information into the model. |
| Outcome: | The proposed model captures structural information of arguments and is able to identify arguments automatically. |
AMEX: Android Multi-annotation Expo Dataset for Mobile GUI Agents (2025.findings-acl)
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Yuxiang Chai, Siyuan Huang, Yazhe Niu, Han Xiao, Liang Liu, Guozhi Wang, Dingyu Zhang, Shuai Ren, Hongsheng Li
| Challenge: | a new dataset is being developed to improve the capabilities of mobile GUI-control agents. |
| Approach: | They propose a dataset designed for generalist mobile GUI-control agents . they use screenshots from popular mobile applications to create a detailed GUI-annotated dataset . |
| Outcome: | The Android Multi-annotation EXpo (AMEX) is a large-scale dataset for generalist mobile GUI-control agents . it includes screenshots from popular mobile applications, which are annotated at multiple levels . |
Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models (2024.acl-long)
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| Challenge: | Mixture-of-Experts (MoE) LLMs achieve higher performance with fewer active parameters, but are still difficult to deploy due to their immense parameter sizes. |
| Approach: | They propose expert-level sparsification techniques to enhance the deployment efficiency of large language models by introducing plug-and-play expert pruning and skipping techniques. |
| Outcome: | The proposed methods reduce model sizes and increase inference speed while maintaining satisfactory performance across a wide range of tasks. |
A Reinforcement Learning Framework for Natural Question Generation using Bi-discriminators (C18-1)
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| Challenge: | Existing research on visual question generation is focused on training models to fit the annotated data set that makes them indifferent from other language generation tasks. |
| Approach: | They propose to use two discriminators to enhance the training of a visual question generator to ask natural questions about an image. |
| Outcome: | The proposed model outperforms state-of-the-art models in terms of automatic and human evaluation metrics. |
A3: Android Agent Arena for Mobile GUI Agents with Essential-State Procedural Evaluation (2026.findings-acl)
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Yuxiang Chai, Shunye Tang, Han Xiao, Weifeng Lin, Hanhao Li, Jiayu Zhang, Liang Liu, Pengxiang Zhao, Guangyi Liu, Guozhi Wang, Shuai Ren, Rongduo Han, Haining Zhang, Siyuan Huang, Hongsheng Li
| Challenge: | Existing evaluation methods for mobile GUI agents rely on static frame assessments or offline static apps. |
| Approach: | They propose an evaluation system that leverages large language models as reward models to verify task completion and process achievement. |
| Outcome: | The proposed system addresses the limitations of traditional function based evaluation methods on online dynamic apps. |
Controllable Contamination Detection for Reliable LLM Evaluation with Statistical Guarantees (2026.acl-long)
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Zheng Zhang, Qi Liu, Siyuan Liang, Ning Li, Zirui Hu, Weibo Gao, Rui Li, Zhenya Huang, Leszek Rutkowski, Baosheng Yu, Dacheng Tao
| Challenge: | Existing training data detectors fail to detect clean samples from contaminated test sets . existing methods fail to identify clean samples due to black-box nature of LLMs . |
| Approach: | They propose a framework that detects and filters contaminated evaluation data . they propose 'failure detection' to reduce the proportion of contaminated samples mistakenly retained . |
| Outcome: | The proposed framework reduces false discovery rate (FDR) under valid FDR control while maintaining evaluation consistency. |
Bridging by Word: Image Grounded Vocabulary Construction for Visual Captioning (P19-1)
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| Challenge: | Existing research on image captioning generates frequent n-grams with irrelevant words. |
| Approach: | They propose to construct an image-grounded vocabulary incorporating visual information and relations among words into the decoding process directly. |
| Outcome: | The proposed framework is compared with state-of-the-art models on MS COCO and Flickr30k and shows that it is more efficient than existing models. |
PreAct: Prediction Enhances Agent’s Planning Ability (2025.coling-main)
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| Challenge: | Existing methods to analyze Markov decision processes (MDPs) are based on chain-of-thought (COT) and historical thought, action, and observation. |
| Approach: | They propose a model that integrates prediction, reasoning, and action with other models to provide a wider range of reasoning and more efficient actions. |
| Outcome: | The proposed model outperforms the ReAct method in completing complex tasks and is more efficient when paired with other memory or selection strategy techniques. |
Locate Then Ask: Interpretable Stepwise Reasoning for Multi-hop Question Answering (2022.coling-1)
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| Challenge: | Existing methods for multi-hop reasoning ignore grounding on supporting facts of each step, which tends to generate inaccurate decompositions. |
| Approach: | They propose an interpretable stepwise reasoning framework that incorporates supporting sentences and questions at each intermediate step and utilizes the inference of the current hop for the next until reasoning out the final result. |
| Outcome: | The proposed model can boost performance and yield a better interpretable reasoning process without decomposition supervision. |
AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator (2025.coling-main)
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| Challenge: | Recent large language models (LLMs) have demonstrated superior performance in static medical question answering benchmarks, rivaling even human experts. |
| Approach: | They propose a multi-agent framework emulating dynamic medical interactions between Doctor as player and NPCs including Patient and Examiner to assess the performance of LLM-driven Doctor agents in simulated clinical scenarios. |
| Outcome: | The proposed framework emulates dynamic medical interactions between Doctor as player and NPCs including Patient and Examiner. |
An Enhanced Knowledge Injection Model for Commonsense Generation (2020.coling-main)
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Zhihao Fan, Yeyun Gong, Zhongyu Wei, Siyuan Wang, Yameng Huang, Jian Jiao, Xuanjing Huang, Nan Duan, Ruofei Zhang
| Challenge: | a recent study shows that digging the relationship of concepts from scratch is non-trivial for commonsense generation tasks. |
| Approach: | They use a retrieve-and-edit framework to retrieve a prototype with these concepts . they use qt and qq to generate commonsense questions at scale . |
| Outcome: | The proposed method significantly improves the performance on commonsense generation tasks. |
Activating Distributed Visual Region within LLMs for Efficient and Effective Vision-Language Training and Inference (2025.acl-long)
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| Challenge: | Existing Large Vision-Language Models (LVLMs) learn visual capacity through visual instruction tuning. |
| Approach: | They propose a method for LVLMs to be trained by selective layers tuning . they propose removing non-critical layers outside the visual region . |
| Outcome: | The proposed approach preserves nearly 99% of visual performance and improves textual task results while reducing training time. |
Boosting LLM Agents with Recursive Contemplation for Effective Deception Handling (2024.findings-acl)
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Shenzhi Wang, Chang Liu, Zilong Zheng, Siyuan Qi, Shuo Chen, Qisen Yang, Andrew Zhao, Chaofei Wang, Shiji Song, Gao Huang
| Challenge: | Recent advances in large language models (LLMs) have led to significant success in using LLMs as agents. |
| Approach: | They propose a cognitive framework that incorporates first-order and second-order perspective transitions into LLMs to enhance their ability to identify and counteract deceptive information. |
| Outcome: | The proposed framework enhances LLMs’ ability to identify and counteract deceptive information without extra fine-tuning and data. |