Papers by Long Hu
FinLFQA: Evaluating Attributed Text Generation of LLMs in Financial Long-Form Question Answering (2025.findings-emnlp)
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| Challenge: | Existing benchmarks focus on simple attribution that retrieves textual evidence as references. |
| Approach: | They propose a benchmark to evaluate the ability of large language models to generate reliable attributions. |
| Outcome: | The proposed benchmark evaluates the ability of LLMs to generate long-form answers with reliable and nuanced attributions. |
WavLLM: Towards Robust and Adaptive Speech Large Language Model (2024.findings-emnlp)
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Shujie Hu, Long Zhou, Shujie Liu, Sanyuan Chen, Lingwei Meng, Hongkun Hao, Jing Pan, Xunying Liu, Jinyu Li, Sunit Sivasankaran, Linquan Liu, Furu Wei
| Challenge: | Recent advances in large language models (LLMs) have expanded their scope to encompass multimodal functions. |
| Approach: | They propose a robust and adaptive speech large language model with dual encoders . they validate the model on universal speech benchmarks and apply it to specialized speech-question-answer datasets based on a CoT approach . |
| Outcome: | The proposed model achieves state-of-the-art performance across a range of speech tasks on the same model size. |
Autoregressive Speech Synthesis without Vector Quantization (2025.acl-long)
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Lingwei Meng, Long Zhou, Shujie Liu, Sanyuan Chen, Bing Han, Shujie Hu, Yanqing Liu, Jinyu Li, Sheng Zhao, Xixin Wu, Helen M. Meng, Furu Wei
| Challenge: | MELLE is a novel language modeling approach for text-to-speech synthesis that generates continuous tokens from text . authors demonstrate that it reduces the need for vector quantization and improves model robustness . |
| Approach: | They propose to autoregressively generate continuous mel-spectrogram frames directly from text condition, bypassing vector quantization. |
| Outcome: | The proposed model achieves superior performance across multiple metrics and is more streamlined. |
Decomposition Dilemmas: Does Claim Decomposition Boost or Burden Fact-Checking Performance? (2025.naacl-long)
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| Challenge: | Fact-checking pipelines adopt the Decompose-Then-Verify paradigm, where texts are broken down into smaller claims for individual verification and subsequently combined for a veracity decision. |
| Approach: | They propose to categorize decomposition errors and to reveal a trade-off between accuracy gains and noise introduced by decomposing. |
| Outcome: | The proposed analysis provides new insights into understanding current system’s instability and offers guidance for future studies toward improving claim decomposition in fact-checking pipelines. |
Multi-level Distillation of Semantic Knowledge for Pre-training Multilingual Language Model (2022.emnlp-main)
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| Challenge: | Existing methods for improving multilingual models did not focus on learning the semantic structure of representation. |
| Approach: | They propose a method to improve multilingual language models by aligning parallel sentences . they propose token-, word-, sentence- and structure-level alignment objectives . |
| Outcome: | The proposed method outperforms baseline models on XNLI, PAWS-X, and XQuAD . it obtains comparable performance on low-resource languages, the authors show . |
Explore More Guidance: A Task-aware Instruction Network for Sign Language Translation Enhanced with Data Augmentation (2022.findings-naacl)
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| Challenge: | Existing studies focus on the recognition step, while paying less attention to sign language translation. |
| Approach: | They propose a task-aware instruction network, namely TIN-SLT, for sign language translation, by introducing the isntruction module and the learning-based feature fuse strategy into a Transformer network. |
| Outcome: | The proposed system outperforms existing solutions on two benchmark datasets, PHOENIX-2014-T and ASLG-PC12, and outperformed previous best solutions by 1.65 and 1.42 in terms of BLEU-4. |
MMAC: A Multilingual, Multimodal Alignment Framework for Cultural Grounding Evaluation (2026.acl-long)
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Weihua Zheng, Zhengyuan Liu, Tanmoy Chakraborty, Weiwen Xu, Xiaoxue Gao, Bryan Chen Zhengyu Tan, Bowei Zou, Chang Liu, Yujia Hu, Xing Xie, Xiaoyuan Yi, Jing Yao, Chaojun Wang, Long Li, Rui Liu, Huiyao Liu, Koji Inoue, Ryuichi Sumida, Tatsuya Kawahara, Fan Xu, Lingyu Ye, Wei Tian, Dongjun Kim, Jimin Jung, Jaehyung Seo, Nadya Yuki Wangsajaya, Pham Minh Duc, Ojasva Saxena, Palash Nandi, Xiyan Tao, Wiwik Karlina, Tuan Luong, Keertana Arun Vasan, Roy Ka-Wei Lee, Nancy F. Chen
| Challenge: | Existing models lack cultural alignment across modalities and languages . a new framework to assess cultural awareness across linguistics and languages is needed . |
| Approach: | They propose a framework that integrates tri-modally aligned cultural benchmarks and a five-dimensional evaluation protocol to assess cross-country awareness disparities. |
| Outcome: | The proposed framework assesses cultural awareness disparities across modalities and languages . it is the first dataset aligned at the input level across text, image, and speech . |
Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph (2022.aacl-main)
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| Challenge: | Existing knowledge graph question answering methods only search for the answer in a large knowledge graph. |
| Approach: | They propose to partition retrieved knowledge subgraphs into smaller sub-KSGs and then use a graph-augmented learning to rank method to select the top-ranked sub-kSGs. |
| Outcome: | The proposed method can capture global interactions in question and subgraphs and local interactions on the full KSG and top-ranked sub-KSGs respectively. |
KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction (2024.acl-long)
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Zixuan Li, Yutao Zeng, Yuxin Zuo, Weicheng Ren, Wenxuan Liu, Miao Su, Yucan Guo, Yantao Liu, Lixiang Lixiang, Zhilei Hu, Long Bai, Wei Li, Yidan Liu, Pan Yang, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
| Challenge: | None. None.. None! |
| Approach: | None. None.. None! |
| Outcome: | None. None. No. : |
When Personalization Legitimizes Risks: Uncovering Safety Vulnerabilities in Personalized Dialogue Agents (2026.acl-long)
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Jiahe Guo, Xiangran Guo, Yulin Hu, Zimo Long, Xingyu Sui, Xuda Zhi, Yongbo Huang, Hao He, Weixiang Zhao, Yanyan Zhao, Bing Qin
| Challenge: | Existing research on personalized LLM agents focuses on the effectiveness of personalized responses. |
| Approach: | They propose a benchmark to quantify intent legitimation in personalized interactions . they propose 'detection-reflection' method that detects intent legititimation from internal representation space . |
| Outcome: | The proposed method reduces safety degradation by using internal representation space. |
Semantic Structure Enhanced Event Causality Identification (2023.acl-long)
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| Challenge: | Existing methods for Event Causality Identification (ECI) capture implicit associations between events, which are difficult because they lack the ability to understand the associations between two events. |
| Approach: | They propose a model that captures the implicit associations between two events and integrates the event-centric structure information into a GNN-based event aggregator. |
| Outcome: | The proposed model improves on three widely used datasets showing that it integrates event-centric and event-associated semantic elements and captures event associations. |
Coordinating Search-Informed Reasoning and Reasoning-Guided Search in Claim Verification (2026.acl-long)
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| Challenge: | Existing methods for multi-hop claim verification require multi-step reasoning to construct verification chains while iterating for information to uncover hidden bridging facts. |
| Approach: | They propose a hierarchical agent reasoning and information search model that integrates reasoning and search-informed reasoning. |
| Outcome: | Experimental results show that HARIS improves multi-hop claim verification accuracy and interpretability. |
Large Language Model-Based Event Relation Extraction with Rationales (2025.coling-main)
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| Challenge: | Existing methods for ERE rely on large language models, but they face limitations. |
| Approach: | They propose an LLM-based approach with rationales for the ERE task . LLMERE transforms ERE into a question-and-answer task that may have multiple answers . |
| Outcome: | Experimental results show that LLMERE improves over existing methods. |
GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs (2026.findings-acl)
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Meixiu Long, Duolin Sun, Dan Yang, Yihan Jiao, Lei Liu, Jiahai Wang, Binbin Hu, Yue Shen, Jie Feng, Zhehao Tan, Junjie Wang, Lianzhen Zhong, Jian Wang, Peng Wei, Jinjie Gu
| Challenge: | Existing rankers excel in lexical-matching scenarios, while they struggle with complex queries requiring deep reasoning. |
| Approach: | They propose a new paradigm that balances flexibility and context awareness to unlock the full potential of groupwise reranking. |
| Outcome: | The proposed approach achieves a state-of-the-art 65.2 NDCG@10 on BRIGHT and surpasses baselines by 2.1 points on R2MED while delivering a 6.4 inference speedup. |
FAIRGAMER: Evaluating Social Biases in LLM-Based Video Game NPCs (2026.acl-long)
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Bingkang Shi, Jen-tse Huang, Luo Long, Tianyu Zong, Hongzhu Yi, Yuanxiang Wang, Songlin Hu, Xiaodan Zhang, Zhongjiang Yao
| Challenge: | Large Language Models (LLMs) have enhanced or replaced traditional non-player characters in video games. |
| Approach: | They propose a benchmark to evaluate social biases across three interaction patterns: transaction, cooperation, and competition. |
| Outcome: | The proposed benchmark assesses four bias types across transaction, cooperation, and competition using a novel metric, FairMCV. |
MINTQA: A Multi-Hop Question Answering Benchmark for Evaluating LLMs on New and Long-tail Knowledge (2026.acl-long)
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| Challenge: | Existing studies have shown that large language models can handle knowledge with varying familiarity. |
| Approach: | They propose a benchmark to evaluate multi-hop question answering on new and tail knowledge . they use RAG to integrate external knowledge into large language models . |
| Outcome: | The proposed benchmark evaluates the multi-hop reasoning ability of large language models . it primarily evaluates their ability to handle knowledge with different levels of familiarity . |