Papers by Han Fu
Large Language Models are good multi-lingual learners : When LLMs meet cross-lingual prompts (2025.coling-main)
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| Challenge: | Experimental results show that Large Language Models can generate rule-based data in long contexts without following all specified rules. |
| Approach: | They propose a novel prompting strategy Multi-Lingual Prompt which automatically translates the error-prone rule that an LLM struggles to follow into another language, thus drawing greater attention to it. |
| Outcome: | The proposed framework outperforms state-of-the-art prompting methods on public datasets across various tasks, with a specific case study in text-to-MIP instances. |
Reference Network for Neural Machine Translation (P19-1)
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| Challenge: | Neural Machine Translation (NMT) generates translations in isolation, resulting in translation inconsistency and ambiguity. |
| Approach: | They propose to incorporate referring process into translation decoding of NMT by using local coordinates coding to obtain global context vectors containing monolingual and bilingual contextual information. |
| Outcome: | The proposed model improves translation quality with lightweight computation cost on Chinese-English and English-German translation tasks. |
CamoQuery: Language-Guided Reasoning Camouflaged Object Segmentation (2026.acl-long)
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| Challenge: | Existing methods for camouflaged object segmentation are limited to vision-only mask prediction under fixed task assumptions. |
| Approach: | They propose a language-guided reasoning camouflaged object segmentation task that generates an intent-consistent segmentation mask from an image and an implicit query text instruction. |
| Outcome: | The proposed task can generate an intent-consistent segmentation mask from a camouflaged image and an implicit query text instruction. |
Towards Human-aligned Evaluation for Linear Programming Word Problems (2024.lrec-main)
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Linzi Xing, Xinglu Wang, Yuxi Feng, Zhenan Fan, Jing Xiong, Zhijiang Guo, Xiaojin Fu, Rindra Ramamonjison, Mahdi Mostajabdaveh, Xiongwei Han, Zirui Zhou, Yong Zhang
| Challenge: | Existing evaluation methodologies for MWPs diverge from human judgment and face challenges in recognizing mathematically equivalent answers. |
| Approach: | They propose an evaluation metric rooted in graph edit distance that features benefits such as permutation invariance and more accurate program equivalence identification. |
| Outcome: | The proposed evaluation metric features benefits such as permutation invariance and more accurate program equivalence identification. |
DeepSynth-Eval: Objectively Evaluating Information Consolidation in Deep Survey Writing (2026.findings-acl)
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Hongzhi Zhang, Yuanze Hu, Tinghai Zhang, Jia Fu, Tao Wang, Junwei Jing, Zhaoxin Fan, Wei Bi, Ruiming Tang, Han Li, Guorui Zhou, Kun Gai
| Challenge: | Large Language Models (LLMs) are evolving towards autonomous agents . retrieval capabilities are well-benchmarked, but post-retrieval synthesis is under-evaluated due to open-ended writing. |
| Approach: | They propose a benchmark to evaluate information consolidation capabilities using survey papers as gold standards. |
| Outcome: | The proposed benchmark analyzes the post-retrieval synthesis stage of large language models . it leverages high-quality survey papers as gold standards and reverse-engineers research requests . the proposed benchmark outperforms single-turn generation and reduces hallucinations . |
AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning (2025.emnlp-demos)
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Zhong Zhang, Yaxi Lu, Yikun Fu, Yupeng Huo, Shenzhi Yang, Yesai Wu, Han Si, Xin Cong, Haotian Chen, Yankai Lin, Xie Xie, Wei Zhou, Wang Xu, Zhou Su, Zhongwu Zhai, Xiaoming Liu, null Meiyudong, Jianming Xu, Hongyan Tian, Chongyi Wang, Chi Chen, Yuan Yao, Zhiyuan Liu, Maosong Sun
| Challenge: | Large language model agents have enabled GUI-based automation, but their deployment is limited by noisy data, poor generalization, and lack of support for non-English GUIs. |
| Approach: | They propose an 8B-parameter GUI agent built for robust and efficient on-device GUI interaction. |
| Outcome: | The proposed GUI agent achieves promising performance on five public benchmarks and proposed Chinese benchmark CAGUI. |
LaTeX2Solver: a Hierarchical Semantic Parsing of LaTeX Document into Code for an Assistive Optimization Modeling Application (2023.acl-demo)
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Rindra Ramamonjison, Timothy Yu, Linzi Xing, Mahdi Mostajabdaveh, Xiaorui Li, Xiaojin Fu, Xiongwei Han, Yuanzhe Chen, Ren Li, Kun Mao, Yong Zhang
| Challenge: | Existing systems that translate optimization formulas manually are cumbersome and time-consuming. |
| Approach: | They propose a system that converts optimization formulas from TeX document to solver language. |
| Outcome: | The proposed system helps operations research practitioners convert optimization formulations into solver modeling languages. |
Fine-grained Knowledge Enhancement for Retrieval-Augmented Generation (2025.findings-acl)
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| Challenge: | Existing studies rely on semantic similarity to retrieve knowledge but ignore fine-grained information within documents. |
| Approach: | They propose a fine-grained knowledge enhancement method to fill knowledge gaps with retrieved external information by a Chain-of-Thought prompting procedure and a decoding enhancement strategy to constrain the document-based decoding process. |
| Outcome: | The proposed method can be applied in a plug-and-play manner to enhance its performance with no additional modules or training process. |
GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking (2025.acl-long)
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Yingjian Chen, Haoran Liu, Yinhong Liu, Jinxiang Xie, Rui Yang, Han Yuan, Yanran Fu, Peng Yuan Zhou, Qingyu Chen, James Caverlee, Irene Li
| Challenge: | Existing fact-checking methods that use large language models often generate subtle factual errors. |
| Approach: | They propose a fact-checking framework that uses extracted knowledge graphs to enhance text representation. |
| Outcome: | GraphCheck outperforms existing specialized fact-checkers on seven benchmarks spanning general and medical domains . Graph Neural Networks process extracted knowledge graphs as a soft prompt, enabling efficient fact- checking in a single inference call. |
Beyond Quantity: Trajectory Diversity Scaling for Code Agents (2026.findings-acl)
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Guhong Chen, Chenghao Sun, Cheng Fu, Qiyao Wang, Zhihong Huang, ChaoPeng Wei, Guangxu Chen, Feiteng Fang, Ahmadreza Argha, Bing Zhao, Xander Xu, Qi Han, Hamid Alinejad-Rokny, Qiang Qu, Binhua Li, Shiwen Ni, Min Yang, HU Wei, Yongbin Li
| Challenge: | Code large language models (LLMs) are becoming tool-interactive agents . quantity-centric scaling exhibits an early bottleneck that underutilizes trajectory data . et al.: a new approach to scale trajectory diversity improves tool-use generalization . |
| Approach: | They propose a Trajectory Diversity Scaling-based data synthesis framework for code agents that scales performance through diversity rather than raw volume. |
| Outcome: | Experiments on general tool-use benchmarks and code agent tasks show that TDScaling improves tool-user generalization and inherent coding proficiency. |
Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs (2025.findings-emnlp)
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| Challenge: | Neural network pruning disrupts LLMs’ internal activation features crucial for lie detection . layer-wise pruning sparsity inadvertently removes crucial weights, failing to improve lie detection performance despite its reliance on the most crucial LLM layer. |
| Approach: | They propose a pruning approach that places greater emphasis on layers with more activation outliers and stronger discriminative features simultaneously. |
| Outcome: | The proposed approach improves the hallucination detection for pruned LLMs (achieving 88% accuracy at 50% sparsity) and enhances their performance on TruthfulQA. |
EssayJudge: A Multi-Granular Benchmark for Assessing Automated Essay Scoring Capabilities of Multimodal Large Language Models (2025.findings-acl)
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Jiamin Su, Yibo Yan, Fangteng Fu, Zhang Han, Jingheng Ye, Xiang Liu, Jiahao Huo, Huiyu Zhou, Xuming Hu
| Challenge: | Automated Essay Scoring (AES) systems face three major challenges: reliance on handcrafted features that limit generalizability, difficulty in capturing fine-grained traits like coherence and argumentation, and inability to handle multimodal contexts. |
| Approach: | They propose a multimodal benchmark to evaluate AES capabilities across lexical-, sentence-, and discourse-level traits without manual feature engineering. |
| Outcome: | The proposed system can evaluate AES capabilities across lexical-, sentence-, and discourse-level traits without manual feature engineering. |
Ensemble Privacy Defense for Knowledge-Intensive LLMs against Membership Inference Attacks (2026.findings-eacl)
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| Challenge: | Large language models (LLMs) are the foundation of modern natural language processing, powering applications across diverse domains. |
| Approach: | They propose a model-agnostic defense framework which aggregates and evaluates the outputs of a knowledge-injected LLM, a base LLM and a dedicated judge model to enhance resistance against membership inference attacks. |
| Outcome: | The proposed framework reduces MIA success by up to 27.8% for SFT and 526.3% for RAG compared to inference-time baseline while maintaining answer quality. |
LoRE-Merging: Exploring Low-Rank Estimation For Large Language Model Merging (2025.findings-emnlp)
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| Challenge: | a framework for model merging is proposed without additional training . task vectors from fine-tuned models exhibit a limited number of dominant singular values . |
| Approach: | They propose a framework for model merging based on low-rank estimation of task vectors without access to the base model. |
| Outcome: | The proposed framework improves models without additional training without additional inputs. |
Ask Question First for Enhancing Lifelong Language Learning (2022.coling-1)
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| Challenge: | Existing approaches to stream learning NLP tasks suffer from catastrophic forgetting and are exacerbated when the previous task’s pseudo data is insufficient. |
| Approach: | They propose to use a new data format to train pseudo questions of previous tasks to stream learning NLP tasks while retaining knowledge of previous ones. |
| Outcome: | The proposed model is more robust to sufficient and insufficient pseudo-data when the task boundary is both clear and unclear. |
Decomposing Complex Questions Makes Multi-Hop QA Easier and More Interpretable (2021.findings-emnlp)
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| Challenge: | Multi-hop QA requires the machine to answer complex questions through finding multiple clues and reasoning, and provide explanatory evidence to demonstrate the reasoning process. |
| Approach: | They propose a three-stage framework based on complex question decomposition that decomposes the complex question, then reads the sub-questions and then performs numerical comparison to get the final answer. |
| Outcome: | The proposed framework achieves state-of-the-art in the 2WikiMultiHopQA dataset, with a winning joint F1 score of 53.58 on the leaderboard. |
MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)
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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie
| Challenge: | Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models. |
| Approach: | They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts. |
| Outcome: | The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings . |
When Truthful Representations Flip Under Deceptive Instructions? (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) follow maliciously crafted instructions to generate deceptive responses, posing safety challenges. |
| Approach: | They use Sparse Autoencoders to analyze LLM's internal representations to determine when and how they "flip" from truthful to deceptive under deceptively crafted instructions. |
| Outcome: | The proposed model's True/False output is predictable across all conditions based on the model''s representation, and the Deceptive instructions induce significant representational shifts compared to Truthful/Neutral representations. |
RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking (2026.findings-acl)
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| Challenge: | Retrieval-Augmented Generation (RAG) integrates knowledge from tables with an external knowledge base to improve the answer relevance and accuracy. |
| Approach: | They propose a table-corpora-aware RAG framework called T-RAG to integrate external knowledge into Large Language Models (LLMs) they then develop a multi-table question answering benchmark called MultiTableQA which spans 3 different task types, 57,193 tables, and 23,758 questions in total. |
| Outcome: | The proposed framework achieves state-of-the-art accuracy, recall, and runtime performance, with improvements of up to 9.4%. |
Compiling Activation Steering into Weights via Null-Space Constraints for Stealthy Backdoors (2026.acl-long)
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Rui Yin, Tianxu Han, Naen Xu, Changjiang Li, Ping He, Chunyi Zhou, Jun Wang, Zhihui Fu, Tianyu Du, Jinbao Li, Shouling Ji
| Challenge: | Existing methods to inject safety-aligned large language models rely on token-level mappings, which do not guarantee sustained harmful output. |
| Approach: | They propose a method that directly modifies model weights to map a trigger to an attacker-specified response. |
| Outcome: | The proposed method achieves high triggered attack success while maintaining non-triggered safety and general utility. |
Chinese MentalBERT: Domain-Adaptive Pre-training on Social Media for Chinese Mental Health Text Analysis (2024.findings-acl)
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| Challenge: | Existing models for language analysis are inadequate for specialized domains like psychology. |
| Approach: | They have enriched a Chinese social media database with psychological lexicons to enhance its applicability to psychological text analysis. |
| Outcome: | The proposed model performed better on six public datasets and provided relevant predictions given the masked sentences. |
Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision? (2025.acl-long)
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| Challenge: | Graph Neural Networks (GNNs) with CLIP pipeline are difficult because of the scarcity of labeled data and text supervision, different levels of downstream tasks, and conceptual gaps between domains. |
| Approach: | They propose a multi-modal prompt learning paradigm to adapt pre-trained GNNs to downstream tasks with weak text supervision. |
| Outcome: | The proposed model can generalize graphs to unseen classes with weak text supervision. |
PEMT: Multi-Task Correlation Guided Mixture-of-Experts Enables Parameter-Efficient Transfer Learning (2024.findings-acl)
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| Challenge: | Parameter-efficient fine-tuning (PEFT) is an effective method for adapting pre-trained language models to various tasks efficiently. |
| Approach: | They propose a parameter-efficient fine-tuning framework that captures transferable knowledge as a weighted combination of adapters trained on source tasks. |
| Outcome: | The proposed method yields stable improvements over full fine-tuning and knowledge transferring methods on a broad range of tasks over 17 datasets. |
Reconstructing Capsule Networks for Zero-shot Intent Classification (D19-1)
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| Challenge: | Existing methods for intent classification are limited due to fast-emerging intents . a recent study shows that existing methods are not effective in recognizing unseen intents. |
| Approach: | They propose to reconstruct capsule networks for zero-shot intent classification by using latent information from labeled utterances. |
| Outcome: | The proposed method outperforms existing methods on two task-oriented dialogue datasets in different languages. |
Quantized but Deceptive? A Multi-Dimensional Truthfulness Evaluation of Quantized LLMs (2025.emnlp-main)
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| Challenge: | Quantization enables efficient deployment of large language models in resource-constrained environments . but impact on truthfulness remains largely unexplored . |
| Approach: | They propose a framework to assess the truthfulness of quantized large language models . they find quantized models retain internally truthful representations but produce false outputs . |
| Outcome: | The framework assesses the truthfulness of quantized models across three dimensions . it finds that quantized model models retain internally truthful representations but are more susceptible to false outputs . |