Papers by Hong Sun
Enhancing Self-Attention with Knowledge-Assisted Attention Maps (2022.naacl-main)
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Jiangang Bai, Yujing Wang, Hong Sun, Ruonan Wu, Tianmeng Yang, Pengfei Tang, Defu Cao, Mingliang Zhang1, Yunhai Tong, Yaming Yang, Jing Bai, Ruofei Zhang, Hao Sun, Wei Shen
| Challenge: | Existing works of knowledge infusion depend on multi-task learning frameworks, which are inefficient and require large-scale retraining when new knowledge is considered. |
| Approach: | They propose a method which integrates knowledge-generated attention maps into the self-attention mechanism and integrates it into the model. |
| Outcome: | The proposed model outperforms existing methods on academic datasets and industry-scale ad relevance applications. |
OpenHuEval: Evaluating Large Language Model on Hungarian Specifics (2025.findings-acl)
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Haote Yang, Xingjian Wei, Jiang Wu, Noémi Ligeti-Nagy, Jiaxing Sun, Yinfan Wang, Győző Zijian Yang, Junyuan Gao, Jingchao Wang, Bowen Jiang, Shasha Wang, Nanjun Yu, Zihao Zhang, Shixin Hong, Hongwei Liu, Wei Li, Songyang Zhang, Dahua Lin, Lijun Wu, Gábor Prószéky, Conghui He
| Challenge: | Recent advances in Large Language Models (LLMs) represent significant strides toward artificial general intelligence (AGI). |
| Approach: | They introduce OpenHuEval, the first benchmark for LLMs focusing on the Hungarian language and specifics. |
| Outcome: | The framework reveals intrinsic patterns and mechanisms of LLMs in non-English languages, with Hungarian serving as an example. |
Uncertainty Guided Label Denoising for Document-level Distant Relation Extraction (2023.acl-long)
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| Challenge: | Document-level relation extraction (DocRE) aims to extract semantic relations between entities in a document. |
| Approach: | They propose a Document-level distant relation extraction framework with unreliable pseudo labels to denoise DS data. |
| Outcome: | The proposed framework outperforms strong baselines on two public datasets. |
Question Generation from SQL Queries Improves Neural Semantic Parsing (D18-1)
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| Challenge: | Using question generation, we learn a semantic parser with 30% of the supervised training data. |
| Approach: | They propose to use question generation to learn a semantic parser with less supervised training data. |
| Outcome: | The proposed method improves the state-of-the-art model with less training data. |
Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples! (2023.findings-emnlp)
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| Challenge: | Large Language Models have made remarkable strides in various tasks, but whether they are competitive few-shot solvers remains an open question. |
| Approach: | They propose an adaptive filter-then-rerank paradigm to combine the strengths of LLMs and SLMs. |
| Outcome: | The proposed system achieves promising improvements on various IE tasks with acceptable time and cost investment. |
DischargeSim: A Simulation Benchmark for Educational Doctor–Patient Communication at Discharge (2025.emnlp-main)
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| Challenge: | Discharge communication is a critical yet underexplored component of patient care, where the goal shifts from diagnosis to education. |
| Approach: | They propose a benchmark that evaluates large language models’ ability to act as personalized discharge educators. |
| Outcome: | Experiments with 18 LLMs show that model size does not always yield better education outcomes, highlighting trade-offs in strategy use and content prioritization. |
SOLAR: Serendipity Optimized Language Model Aligned for Recommendation (2025.findings-emnlp)
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Zichen Yuan, Lifan Sun, Yucen Zhuang, Yue Wang, Xinyuan Song, Tianqi Xu, Siyuan Li, Junchen Fu, Youhua Li, Sirui Hong, Jiaqi Chen, Joemon M. Jose, Yongxin Ni
| Challenge: | Large Language Models have shown strong potential in recommendation tasks . however, their application to serendipity-oriented recommendations remains challenging . |
| Approach: | They propose a domain-adaptive instruction tuning method that aligns Large Language Models with recommendation tasks. |
| Outcome: | The proposed framework bridges the domain gap between LLMs and recommendation tasks. |
BBTv2: Towards a Gradient-Free Future with Large Language Models (2022.emnlp-main)
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| Challenge: | Recent work on parameter-efficient tuning (PET) only tunes a small portion of parameters while keeping most of the parameters of the LLM unchanged. |
| Approach: | They propose an improved version of Black-Box Tuning to tune PTMs through gradient descent . they prepend continuous prompts to every layer of the PTM and propose a divide-and-conquer gradient-free algorithm to optimize the prompts alternately. |
| Outcome: | The proposed method achieves comparable performance to full model tuning and state-of-the-art parameter-efficient methods under few-shot settings while maintaining much fewer tunable parameters. |
Adversarial Alignment with Anchor Dragging Drift (A3D2): Multimodal Domain Adaptation with Partially Shifted Modalities (2025.acl-long)
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| Challenge: | Domain adaptation is underexplored in multimodal learning environments due to expensive data collection and annotation. |
| Approach: | They propose a bi-alignment scheme to perform drift-drift and anchor-driving matching with partially shifting anchors. |
| Outcome: | The proposed approach achieves superior performance compared with state-of-the-art approaches. |
Using Subtext to Enhance Generative IDRR (2025.acl-short)
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| Challenge: | Arguments contain subtexts, but they are connotative and need prompts to be recognized . a lightweight subtext generator is helpful when the prompt doesn't raise a complex CoT. |
| Approach: | They leverage LLaMA to generate subtexts for argument pairs and verify their effectiveness . they construct a baseline IDRR using the decoder-only backbone LLama . |
| Outcome: | The proposed approach achieves higher F1 scores on two benchmarks than previous models. |
Emma-X: An Embodied Multimodal Action Model with Grounded Chain of Thought and Look-ahead Spatial Reasoning (2025.acl-long)
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| Challenge: | Visual-Language-Action models lack the ability to generate actionable policies tailored to specific robotic embodiments. |
| Approach: | They propose an embodied multimodal action model with Grounded Chain of Thought and Look-ahead Spatial Reasoning that enhances spatial reasoning and task planning. |
| Outcome: | The proposed model improves on existing baselines in tasks requiring spatial reasoning and grounding reasoning. |
DUET: Joint Exploration of User–Item Profiles in Recommendation System (2026.findings-acl)
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Yue Chen, Yifei Sun, Lu Wang, Fangkai Yang, Pu Zhao, Minjie Hong, Yifei Dong, Minghua He, Nan Hu, Jianjin Zhang, Zhiwei Dai, Yuefeng Zhan, Weihao Han, Hao Sun, Qingwei Lin, Weiwei Deng, Feng Sun, Qi Zhang, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Existing LLMs are opaque and difficult to interpret, resulting in limited interpretability. |
| Approach: | They propose an interaction-aware profile generator that jointly produces user and item profiles conditioned on both user history and item evidence. |
| Outcome: | The proposed model outperforms baselines on three real-world datasets. |
The Right Time Matters: Data Arrangement Affects Zero-Shot Generalization in Instruction Tuning (2025.findings-acl)
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Bingxiang He, Ning Ding, Cheng Qian, Jia Deng, Ganqu Cui, Lifan Yuan, Haiwen Hong, Huan-ang Gao, Longtao Huang, Hui Xue, Huimin Chen, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing work on instruction tuning has focused on task level, without considering that tasks are artificially defined and, to LLMs, merely consist of tokens and representations. |
| Approach: | They propose a training data arrangement framework that allows for continual learning and loss reduction. |
| Outcome: | The proposed framework promotes continual learning and loss reduction on unseen tasks. |
Amanda: Adaptively Modality-Balanced Domain Adaptation for Multimodal Emotion Recognition (2024.findings-acl)
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| Challenge: | Emotion recognition is a multimodal learning method that can be used for data scarcity. |
| Approach: | They propose to use Adaptively modality-balanced domain adaptation to balance the alignment of different modalities for multimodal emotion recognition. |
| Outcome: | The proposed model outperforms competing models on common datasets on multimodal emotion recognition. |
Third-Person Appraisal Agent: Simulating Human Emotional Reasoning in Text with Large Language Models (2025.findings-emnlp)
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| Challenge: | Emotional reasoning is essential for improving human-AI interactions, especially in mental health support and empathetic systems. |
| Approach: | They propose a third-person appraisal agent that simulates human-like emotional reasoning through three phases: Primary Appraisal, Secondary Appraisals, and Reappraisal. |
| Outcome: | The proposed model outperforms baseline LLMs in various emotional reasoning tasks, demonstrating superior generalization and interpretability. |
User Feedback Alignment for LLM-powered Exploration in Large-scale Recommendation Systems (2025.acl-industry)
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Jianling Wang, Yifan Liu, Yinghao Sun, Xuejian Ma, Yueqi Wang, He Ma, Zhengyang Su, Minmin Chen, Mingyan Gao, Onkar Dalal, Ed H. Chi, Lichan Hong, Ningren Han, Haokai Lu
| Challenge: | Large Language Models (LLMs) can be used to broaden user experiences beyond established preferences and reinforce feedback loops. |
| Approach: | They propose a hierarchical approach that combines hierarchic planning with LLM inference-time scaling to improve recommendation relevancy without compromising novelty. |
| Outcome: | The proposed approach shows significant gains in both user satisfaction and exploration diversity. |
GRADA: Graph-based Reranking against Adversarial Documents Attack (2025.emnlp-main)
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Jingjie Zheng, Aryo Pradipta Gema, Giwon Hong, Xuanli He, Pasquale Minervini, Youcheng Sun, Qiongkai Xu
| Challenge: | Retrieval Augmented Generation (RAG) frameworks are susceptible to adversarial attacks that manipulate the retrieval process by introducing documents that are adversarially similar to the query. |
| Approach: | They propose a framework that integrates external retrieval modules into RAG frameworks to improve the factual accuracy of large language models. |
| Outcome: | The proposed framework reduces adversarial attacks by 80% while maintaining minimal loss in accuracy. |
DetectiveNN: Imitating Human Emotional Reasoning with a Recall-Detect-Predict Framework for Emotion Recognition in Conversations (2024.findings-emnlp)
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| Challenge: | Existing methods for Emotion Recognition in conversations are insufficient in understanding the rich historical emotional context. |
| Approach: | They propose a novel model that utilizes a "recall-detect-predict" framework to imitate human emotional reasoning by 'recalling' past interactions of a speaker to collect emotional cues. |
| Outcome: | The proposed model outperforms existing methods on three benchmark datasets and significantly outperformed existing methods. |
CVAE-based Re-anchoring for Implicit Discourse Relation Classification (2021.findings-emnlp)
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| Challenge: | Existing studies show that training implicit discourse relation classifiers suffers from data sparsity. |
| Approach: | They propose a re-anchoring strategy to reduce the risk of erroneous sampling . they use Conditional VAE to estimate the risk and migrate the anchor to reduce it . |
| Outcome: | The proposed method improves the baseline classifier performance on PDTB v2.0 . |
Parrot: A Training Pipeline Enhances Both Program CoT and Natural Language CoT for Reasoning (2025.emnlp-main)
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Senjie Jin, Lu Chen, Zhiheng Xi, Yuhui Wang, Sirui Song, Yuhao Zhou, Xinbo Zhang, Peng Sun, Hong Lu, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing work focuses on enabling models to generate natural language chain-of-thought rationales or leverage executable and verifiable code, such as Python. |
| Approach: | They propose a novel training pipeline that integrates sequential P-CoT and N-Co T generation and a subtask hybrid training strategy to facilitate natural language transferability. |
| Outcome: | The proposed training pipeline improves both N-CoT and P-Co T performance over the RL baseline. |
Infinite Babble: Inflating 3D Vision-Language Model Inference Overhead via Adversarial Geometric Perturbation (2026.findings-acl)
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| Challenge: | 3D Vision-Language Models (VLMs) are critical cognitive backbone for spatial intelligence, but their reliance on autoregressive decoding introduces a fundamental vulnerability regarding inference efficiency. |
| Approach: | They propose a framework that triggers computational and economic exhaustion in 3D-VLMs by injecting imperceptible noise that forces the model into a state of pathological verbosity. |
| Outcome: | The proposed framework amplifies output length and energy consumption by up to 6.45, demonstrating a potent capability to drain system resources. |
CoLLiE: Collaborative Training of Large Language Models in an Efficient Way (2023.emnlp-demo)
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Kai Lv, Shuo Zhang, Tianle Gu, Shuhao Xing, Jiawei Hong, Keyu Chen, Xiaoran Liu, Yuqing Yang, Honglin Guo, Tengxiao Liu, Yu Sun, Qipeng Guo, Hang Yan, Xipeng Qiu
| Challenge: | Large language models (LLMs) are increasingly pivotal in a wide range of tasks . however, the resources required for training these models necessitate efficient solutions . |
| Approach: | They propose a library that facilitates collaborative training of large language models . they use 3D parallelism, parameter-efficient fine-tuning methods and optimizers . |
| Outcome: | The proposed library has proven superior training efficiency in comparison with prevalent solutions in pre-training and fine-tuning scenarios. |
Preference Heads in Large Language Models: A Mechanistic Framework for Interpretable Personalization (2026.acl-long)
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Weixu Zhang, Ye Yuan, Changjiang Han, Yuxing Tian, Zipeng Sun, Linfeng Du, Jikun Kang, Hong Kang, Xue Liu, Haolun Wu
| Challenge: | Large Language Models exhibit strong implicit personalization ability, but most approaches treat this behavior as a black box. |
| Approach: | They propose a mechanistic interpretation perspective and propose 'sparse' set of Preference Heads . they compute a Preference Contribution Score for each attention head and compare their predictions . |
| Outcome: | The proposed framework computes a Preference Contribution Score (PCS) for each attention head and measures its causal impact on user aligned outputs. |
Why Steering Works: Toward a Unified View of Language Model Parameter Dynamics (2026.acl-long)
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Ziwen Xu, Chenyan WU, Hengyu Sun, Haiwen Hong, Mengru Wang, Yunzhi Yao, Longtao Huang, Hui Xue, Shumin Deng, Zhixuan Chu, Huajun Chen, Ningyu Zhang
| Challenge: | Methods for controlling large language models (LLMs) are often studied in isolation, obscuring connections and making comparison difficult. |
| Approach: | They propose a preference-utility analysis that separates control effects into preference and utility, and measures both on a shared log-odds scale using polarity-paired contrastive examples. |
| Outcome: | The proposed approach improves preference while preserving utility. |
Few-shot Joint Multimodal Aspect-Sentiment Analysis Based on Generative Multimodal Prompt (2023.findings-acl)
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| Challenge: | Existing studies require massive labeled data to train models for multimodal data analysis. |
| Approach: | They propose a novel multimodal prompt model that captures specific aspect terms in a few-shot scenario. |
| Outcome: | The proposed model outperforms baselines on two MABSA-related tasks on a few-shot dataset. |