Papers by Hui Xiong
SiLP: Enhancing Non-Dominant Language Capabilities with a Selective Bidirectional Language Projection Framework (2026.acl-long)
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| Challenge: | Existing methods to improve performance of large language models rely on additional training objectives or language-specific parameters. |
| Approach: | They propose a bidirectional language projection framework that enables efficient multilingual alignment and language shift using the intrinsic parameters. |
| Outcome: | The proposed framework improves performance of non-dominant languages and improves internal representations. |
Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models (2026.findings-acl)
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Hengyuan Zhang, Zhihao Zhang, Ercong Nie, Mingyang Wang, Zunhai Su, Yiwei Wang, Qianli Wang, Shuzhou Yuan, Xufeng Duan, Qibo Xue, Zeping Yu, Chenming Shang, Xiao Liang, Jing Xiong, Hui Shen, Chaofan Tao, Zhengwu Liu, Senjie Jin, Zhiheng Xi, Dongdong Zhang, Sophia Ananiadou, Tao Gui, Ruobing Xie, Hayden Kwok-Hay So, Hinrich Schuetze, Xuanjing Huang, Qi Zhang, Ngai Wong
| Challenge: | Existing literature on mechanistic interpretation (MI) treats it as an observational science, leaving practical applications underexplored. |
| Approach: | They propose a survey structured around the pipeline to identify and improve MI models. |
| Outcome: | The proposed framework enables tangible improvements in Alignment, Capability, and Efficiency. |
Temporal Scaling Law for Large Language Models (2025.emnlp-main)
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Yizhe Xiong, Xiansheng Chen, Xin Ye, Hui Chen, Zijia Lin, Haoran Lian, Zhenpeng Su, Wei Huang, Jianwei Niu, Jungong Han, Guiguang Ding
| Challenge: | Existing studies have found that the test loss of LLMs scales as power-laws with model size, computational budget, and dataset size. |
| Approach: | They propose a concept of Temporal Scaling Law to study test loss of LLMs . they break down test loss into fine-grained token positions and develop a dynamic hyperbolic-law . |
| Outcome: | The proposed model predicts the test loss of LLMs as the training steps scale up. |
Joint Intent Detection and Entity Linking on Spatial Domain Queries (2020.findings-emnlp)
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| Challenge: | Spatial domain queries have unique properties making them more challenging for language understanding than common conversational queries. |
| Approach: | They propose a language understanding framework for spatial domain queries that jointly learns the intent detection and entity linking tasks on a voice assistant service. |
| Outcome: | The proposed framework outperforms baseline methods with a significant margin. |
CartesianMoE: Boosting Knowledge Sharing among Experts via Cartesian Product Routing in Mixture-of-Experts (2025.naacl-long)
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Zhenpeng Su, Xing W, Zijia Lin, Yizhe Xiong, Minxuan Lv, Guangyuan Ma, Hui Chen, Songlin Hu, Guiguang Ding
| Challenge: | Large language models (LLMs) have been attracting much attention due to their impressive performance in all kinds of downstream tasks. |
| Approach: | They propose a mix-of-experts model that allows the model size to grow without raising training costs. |
| Outcome: | The proposed model outperforms existing models in perplexity and robustness tests. |
A Table-to-Text Framework with Heterogeneous Multidominance Attention and Self-Evaluated Multi-Pass Deliberation (2023.findings-emnlp)
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| Challenge: | Table-to-text works have been widely applied in different domains, such as weather forecast and financial report generation. |
| Approach: | They propose a table-to-text approach on top of Self-evaluated multi-pass Generation and Heterogenous Multidominance Attention to explore the hierarchical structure. |
| Outcome: | The proposed method outperforms several SOTA methods quantitatively and qualitatively on three public datasets. |
ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection (2026.findings-acl)
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Yibo Yan, Shen Wang, Jiahao Huo, Hang Li, Boyan Li, Jiamin Su, Xiong Gao, YiFan Zhang, Tianlong Xu, Zhendong Chu, Aoxiao Zhong, Kun Wang, Hui Xiong, Philip S. Yu, Xuming Hu, Qingsong Wen
| Challenge: | Current mathematical benchmarks focus on evaluating MLLMs’ problem-solving ability, yet there is a crucial gap in addressing more complex scenarios such as error detection. |
| Approach: | They propose to evaluate multimodal error detection by evaluating two sub-tasks error step identification and error categorization. |
| Outcome: | The proposed task evaluates MLLMs' ability to handle multimodal questions compared to text-only models. |
Improve Dense Passage Retrieval with Entailment Tuning (2024.emnlp-main)
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| Challenge: | Existing methods for dense retrieval have demonstrated remarkable performance in IR tasks. |
| Approach: | They propose a method to improve the embedding of dense retrievers by using existence claim as a bridge. |
| Outcome: | The proposed method can be plugged into current dense retrieval methods and the results are published in the journal Nature. |
LongFaith: Enhancing Long-Context Reasoning in LLMs with Faithful Synthetic Data (2025.findings-acl)
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| Challenge: | Long-context processing ability has emerged as a significant challenge for large language models. |
| Approach: | They propose a pipeline for synthesizing faithful long-context reasoning instruction datasets . they integrate ground truth and citation-based reasoning prompts integrating them . |
| Outcome: | The proposed pipeline eliminates distractions and improves reasoning chains. |
SceneLM: 3D-Aware Language Models for Editable 3D Scene Synthesis (2026.findings-acl)
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| Challenge: | Existing methods for synthesising 3D scenes from a single image are text-driven and lack precise metric understanding from images. |
| Approach: | They propose a language-model-based framework that grounds 3D scene synthesis in visual evidence by recovering an executable metric 3D layout directly from a single image. |
| Outcome: | The proposed framework recovers an executable metric 3D layout directly from an RGB image and instantiates, places, and edits objects for iterative refinement. |
Anti-Length Shift: Dynamic Outlier Truncation for Training Efficient Reasoning Models (2026.acl-long)
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Wei Wu, Liyi Chen, Congxi Xiao, Tianfu Wang, Qimeng Wang, Chengqiang Lu, Yan Gao, null Yiwu, Yao Hu, Hui Xiong
| Challenge: | Existing efficient reasoning methods rely on explicit length penalties for excessive verbosity on simple queries. |
| Approach: | They propose a training-time intervention that selectively suppresses redundant tokens . they find length shift occurs when models generate unnecessary reasoning on trivial inputs - a phenomenon that is often unexplored . |
| Outcome: | The proposed method reduces inference token usage by 78% while increasing accuracy compared to the initial policy and surpasses state-of-the-art efficient reasoning methods. |
Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating Approach (2022.emnlp-main)
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| Challenge: | Currently, the generalization issues hinder the applicability of neural table-to-text models due to the limited source tables. |
| Approach: | They propose a table-structureaware text generation model with pretrained language model and propose TASD to bridge the gap between the structured table and text input. |
| Outcome: | The proposed model bridges the gap between the structured table and text input and generates accurate and fluent descriptive texts on two public datasets. |
TP-RAG: Benchmarking Retrieval-Augmented Large Language Model Agents for Spatiotemporal-Aware Travel Planning (2025.emnlp-main)
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| Challenge: | Existing studies on large language models (LLMs) focus on basic plan validity, but neglect critical aspects such as route efficiency, POI appeal, and real-time adaptability. |
| Approach: | They propose a benchmark for retrieval-augmented, spatiotemporal-aware travel planning that integrates retrieved trajectories with LLMs’ intrinsic reasoning. |
| Outcome: | The proposed framework improves spatial efficiency and POI rationality while challenging universality and robustness due to conflicting references and noisy data. |
LLM-Based Agent Society Investigation: Collaboration and Confrontation in Avalon Gameplay (2024.emnlp-main)
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Yihuai Lan, Zhiqiang Hu, Lei Wang, Yang Wang, Deheng Ye, Peilin Zhao, Ee-Peng Lim, Hui Xiong, Hao Wang
| Challenge: | Existing studies on LLM agents' social behaviors are lacking . previous studies focused on positive social behaviors, leaving research on negative social behaviors relatively scarce. |
| Approach: | They propose a framework that features a multi-agent system facilitating efficient communication and interaction with LLM agents. |
| Outcome: | The proposed framework is based on Avalon and evaluates on game success and analyzes agents’ social behaviors. |
Mitigating Hallucinations in Multi-modal Large Language Models via Image Token Attention-Guided Decoding (2025.naacl-long)
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| Challenge: | Multi-modal large language models (MLLMs) generate plausible but incorrect content, resulting in hallucinations . recent advances in MLLM technology have demonstrated their outstanding performance in a variety of visual tasks, such as object detection. |
| Approach: | They propose a plug-and-play method which leverages MLLMs’ internal representations to mitigate hallucinations by analyzing input and output tokens. |
| Outcome: | The proposed method exploits MLLMs’ internal representations to mitigate hallucinations. |
Select2Reason: Efficient Instruction-Tuning Data Selection for Long-CoT Reasoning (2026.findings-acl)
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| Challenge: | Large reasoning models exhibit human-like behaviors such as exploration, verification, reflection, and correction. |
| Approach: | They propose a supervised fine-tuning framework for long chain-of-thoughts reasoning . they leverage a difficulty-aware reward model to estimate the learning value of questions . |
| Outcome: | The proposed framework performs fine-tuning on large reasoning models on 10% of the data selected. |
Cross-modality Data Augmentation for End-to-End Sign Language Translation (2023.findings-emnlp)
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| Challenge: | End-to-end sign language translation (SLT) aims to convert sign language videos into spoken language texts without intermediate representations. |
| Approach: | They propose a cross-modality data-augmented framework to transfer gloss-to-text translation capabilities to end-to end sign language translation. |
| Outcome: | The proposed framework outperforms baseline models on two widely used SLT datasets. |
GenDis: Generative-Discriminative Dual-View Co-Training for Generalized Category Discovery (2026.acl-long)
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| Challenge: | Existing methods rely on one-hot discriminative supervision, leading to overfitting on seen classes and poor generalization to unseen ones. |
| Approach: | They propose a Generative–Discriminative Dual-View Co-Training framework that unifies discriminative classification and semantic label generation within an LLM. |
| Outcome: | The proposed framework outperforms existing methods on five benchmarks on the generalized category discovery (GCD) task. |
TokenSelect: Efficient Long-Context Inference and Length Extrapolation for LLMs via Dynamic Token-Level KV Cache Selection (2025.emnlp-main)
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| Challenge: | Rapid advances in Large Language Models have spurred demand for processing extended context sequences . however, performance degradation due to sequence lengths out-of-distribution and excessively long inference times are limiting LLMs in long-context scenarios. |
| Approach: | They propose a training-free method for efficient and accurate long-context inference . they selectively involves a few critical KV cache tokens in attention calculation . |
| Outcome: | The proposed method speeds up attention computation and accelerates inference time while reducing selection overhead. |
Fast Quiet-STaR: Thinking Without Thought Tokens (2025.findings-emnlp)
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| Challenge: | Large Language Models have achieved impressive performance across a range of tasks, but further gains require more than scaling up model sizes or training data. |
| Approach: | They propose a method that gradually reduces the number of thought tokens . this method allows models to internalize more abstract reasoning processes . |
| Outcome: | The proposed framework preserves the benefits of token-level reasoning while reducing computational cost. |
MEIT: Multimodal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation (2025.findings-acl)
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Zhongwei Wan, Che Liu, Xin Wang, Chaofan Tao, Hui Shen, Jing Xiong, Rossella Arcucci, Huaxiu Yao, Mi Zhang
| Challenge: | Recent studies have focused on classifying cardiac conditions using ECG data but have overlooked ECG report generation, which is time-consuming and requires clinical expertise. |
| Approach: | They propose a Multimodal ECG Instruction Tuning framework that extends the capability of large language models (LLMs) for the task. |
| Outcome: | The proposed framework outperforms open-source LLMs and LLM backbones across two large-scale ECG datasets. |
Breaking the Stage Barrier: A Novel Single-Stage Approach to Long Context Extension for Large Language Models (2025.coling-main)
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Haoran Lian, Junmin Chen, Wei Huang, Yizhe Xiong, Wenping Hu, Guiguang Ding, Hui Chen, Jianwei Niu, Zijia Lin, Fuzheng Zhang, Di Zhang
| Challenge: | Recent studies show that Large language models struggle with handling long token sequences due to limited training context size. |
| Approach: | They propose a single-stage continual pretraining method to equip LLMs with long context modeling capabilities. |
| Outcome: | The proposed method outperforms existing methods on 4 language modeling benchmarks. |
Refiner: Restructure Retrieved Content Efficiently to Advance Question-Answering Capabilities (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are limited by their parametric knowledge, leading to hallucinations in knowledge-extensive tasks. |
| Approach: | They propose an end-to-end extract-and-restructure paradigm that leverages a single decoder-only LLM to adaptively extract query-relevant contents verbatim along with the necessary context. |
| Outcome: | Experiments show that a trained Refiner outperforms state-of-the-art RAG and compressing approaches in multiple tasks. |
DSMoE: Matrix-Partitioned Experts with Dynamic Routing for Computation-Efficient Dense LLMs (2025.emnlp-main)
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Minxuan Lv, Zhenpeng Su, Leiyu Pan, Yizhe Xiong, Zijia Lin, Hui Chen, Wei Zhou, Jungong Han, Guiguang Ding, Wenwu Ou, Di Zhang, Kun Gai, Songlin Hu
| Challenge: | Existing sparsification methods like pruning can lose model knowledge through parameter removal. |
| Approach: | They propose a novel approach that achieves sparsification by partitioning pre-trained FFN layers into computational blocks. |
| Outcome: | The proposed approach achieves superior performance across language modeling and downstream tasks under equivalent computational constraints. |
LLM Sensitivity Evaluation Framework for Clinical Diagnosis (2025.coling-main)
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| Challenge: | Existing studies on the sensitivity of Large Language Models (LLMs) to irrelevant contexts neglect the importance of key information. |
| Approach: | They investigate the sensitivity of large language models to key medical information by introducing different perturbation strategies to investigate their sensitivity. |
| Outcome: | The proposed models are based on three LLMs, namely GPT-3.5, GPT-4, Gemini, Claude3 and LLaMA2-7b, and demonstrate their reliability and sensitivity to medical information. |
Explaining Length Bias in LLM-Based Preference Evaluations (2025.findings-emnlp)
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Zhengyu Hu, Linxin Song, Jieyu Zhang, Zheyuan Xiao, Tianfu Wang, Zhengyu Chen, Nicholas Jing Yuan, Jianxun Lian, Kaize Ding, Hui Xiong
| Challenge: | a preference evaluation metric is often biased towards longer responses, revealing a reliability problem . a decomposition of the preference evaluation into two components is needed to understand this bias. |
| Approach: | They propose to decompose the preference evaluation metric into two key components . the first component is length-dependent and related to trustworthiness . |
| Outcome: | The proposed evaluation metric is based on two components: desirability and information mass. |