Papers by Yifan Hu
Towards Faithful Dialogues via Focus Learning (2023.acl-long)
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| Challenge: | Existing knowledge-grounded models rely on elaborate data engineering or increasing the model’s parameters ignoring to track the tokens that significantly influence losses, which is decisive for the optimization direction of the model in each iteration. |
| Approach: | They propose a novel learning approach that adjusts the contribution of each token to the optimization direction by directly scaling the corresponding objective loss. |
| Outcome: | The proposed approach achieves the new state-of-the-art results and generates more reliable responses while maintaining training stability. |
TellWhisper: Tell Whisper Who Speaks When (2026.acl-long)
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| Challenge: | Existing approaches decouple temporal modeling and speaker modeling when addressing 'when' and 'who' . a new framework that couples temporal structure with speaker dynamics is proposed to address these limitations . |
| Approach: | They propose a framework that couples temporal and speaker identity within the speech encoder . they propose TS-RoPE, a time-speaker rotary positional encoding that partitions Query/Key channels into temporal, speaker subspaces and applies region-specific rotations to align "when" and "who" cues in selfattention. |
| Outcome: | The proposed framework couples temporal structure with speaker dynamics in speech encoder . it uses frame-level speaker activity to estimate speaker-activity estimates . |
Complex Numerical Reasoning with Numerical Semantic Pre-training Framework (2025.emnlp-main)
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| Challenge: | Numerical knowledge graphs (NKGs) are not limited to discrete entity-relation knowledge. |
| Approach: | They propose to combine numerical values and entities to solve multi-hop complex reasoning over incomplete knowledge graphs. |
| Outcome: | The proposed approach handles up to 102 types of complex numerical reasoning queries on three public datasets. |
VoiceTextBlender: Augmenting Large Language Models with Speech Capabilities via Single-Stage Joint Speech-Text Supervised Fine-Tuning (2025.naacl-long)
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Yifan Peng, Krishna C Puvvada, Zhehuai Chen, Piotr Zelasko, He Huang, Kunal Dhawan, Ke Hu, Shinji Watanabe, Jagadeesh Balam, Boris Ginsburg
| Challenge: | Recent studies have augmented large language models (LLMs) with speech capabilities, leading to the development of speech language models. |
| Approach: | They propose a single-stage joint speech-text SFT approach for training SpeechLMs . their model combines text-only SFT data with three types of speech-related data . |
| Outcome: | The proposed model outperforms previous SpeechLMs on speech-based QA tasks while maintaining original speech-only capabilities. |
TNT: Text Normalization based Pre-training of Transformers for Content Moderation (2020.emnlp-main)
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| Challenge: | Language model pre-training (self-supervised or unsupervised learning) has been widely used in a multitude of language processing tasks such as named entity recognition, sentiment analysis, question answering and content moderation. |
| Approach: | They propose a new language pre-training model TNT for content moderation that uses a combination of masking strategy and text normalization to learn from text. |
| Outcome: | The proposed model outperforms baselines on hate speech classification task and is a potential approach to misspelling correction. |
Logical Consistency as a Bridge: Improving LLM Hallucination Detection via Label Constraint Modeling between Responses and Self-Judgments (2026.acl-long)
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Hao Mi, Qiang Sheng, Shaofei Wang, Beizhe Hu, Yifan Sun, Zhengjia Wang, Hengqi Zeng, Yang Li, Danding Wang, Juan Cao
| Challenge: | Existing methods for hallucination detection focus on implicit neural uncertainty or explicit symbolic reasoning, ignoring factual hallucinosities. |
| Approach: | They propose a framework that bridges neural features and symbolic judgments for hallucination detection by leveraging a "meta-judgment" process to map symbolic labels back into the feature space. |
| Outcome: | Extensive experiments on 4 public datasets, across 4 LLMs, against 8 baselines demonstrate the superiority of LaaB. |
Adversarial Preference Optimization: Enhancing Your Alignment via RM-LLM Game (2024.findings-acl)
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| Challenge: | Existing methods for training large language models require additional annotations to adjust to shifted distributions. |
| Approach: | They propose an algorithm that allows LLMs and reward models to update alternatively via a min-max game to improve their alignment. |
| Outcome: | The proposed framework improves existing alignment baselines in terms of LLM helpfulness and harmlessness. |
SLAM-Omni: Timbre-Controllable Voice Interaction System with Single-Stage Training (2025.findings-acl)
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Wenxi Chen, Ziyang Ma, Ruiqi Yan, Yuzhe Liang, Xiquan Li, Ruiyang Xu, Zhikang Niu, Yanqiao Zhu, Yifan Yang, Zhanxun Liu, Kai Yu, Yuxuan Hu, Jinyu Li, Yan Lu, Shujie Liu, Xie Chen
| Challenge: | a new spoken dialogue system with single-stage training is demonstrating its low latency and high quality . SLAM-Omni achieves zero-shot timbre control by modeling spoken language with semantic tokens . |
| Approach: | They propose a timbre-controllable, end-to-end voice interaction system with single-stage training. |
| Outcome: | The proposed system outperforms previous models on 4 GPUs with limited data. |
Repulsive Attention: Rethinking Multi-head Attention as Bayesian Inference (2020.emnlp-main)
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Bang An, Jie Lyu, Zhenyi Wang, Chunyuan Li, Changwei Hu, Fei Tan, Ruiyi Zhang, Yifan Hu, Changyou Chen
| Challenge: | Existing studies show that multi-head attention is an effective module in deep neural networks, but there are no explicit mechanisms guaranteeing this property. |
| Approach: | They propose a non-parametric approach that explicitly improves the repulsiveness in multi-head attention and consequently strengthens model’s expressiveness. |
| Outcome: | The proposed approach improves the repulsiveness in multi-head attention and strengthens model’s expressiveness. |
Temporal Flattening in LLM-Generated Text: Comparing Human and LLM Writing Trajectories (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are increasingly used in daily applications, from content generation to code writing. |
| Approach: | They construct a longitudinal dataset of 412 human authors and 6,086 documents spanning 2012–2024 and compare them to trajectories generated by three representative LLMs. |
| Outcome: | The results show that LLMs produce greater lexical diversity but exhibit substantially reduced semantic and cognitive–emotional drift relative to humans. |
Mitigating Hallucinations in Large Vision-Language Models by Self-Injecting Hallucinations (2025.findings-emnlp)
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| Challenge: | Existing methods for hallucination mitigation are based on external dependency and require external annotations or auxiliary models for preference data collection. |
| Approach: | a new method is proposed to help model-generated hallucinations without external dependencies. |
| Outcome: | a new method that self-injects hallucinations into a generated response improves halluuutations mitigation. |
Tailoring Rumor Debunking to You: Diversifying Chinese Rumor-Debunking Passages with an LLM-Driven Simulated Feedback-Enhanced Framework (2026.eacl-industry)
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| Challenge: | Existing methods for fact-checking lack coherence and context, whereas abstractive methods lack cohesion and context. |
| Approach: | They propose a framework that generates Chinese user-specific debunking passages . they propose to use a generative AI framework to generate context-sensitive responses . |
| Outcome: | The proposed framework generates Chinese user-specific debunking passages by iteratively refining outputs based on simulated user feedback. |
Unraveling Interwoven Roles of Large Language Models in Authorship Privacy: Obfuscation, Mimicking, and Verification (2025.emnlp-main)
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| Challenge: | Recent advances in large language models have been driven by large-scale training corpora drawn from diverse sources such as websites, news articles, and books. |
| Approach: | They propose a framework for analyzing dynamic relationships among LLM-enabled AO, AM, and AV in the context of authorship privacy. |
| Outcome: | The proposed framework analyzes the dynamic relationships among LLM-enabled AO, AM, and AV in the context of authorship privacy. |
BERT-Beta: A Proactive Probabilistic Approach to Text Moderation (2021.emnlp-main)
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| Challenge: | Existing approaches to text moderation are reactive and do not account for user generated content. |
| Approach: | They propose a text toxicity propensity model to characterize extent to which a user generated text attracts toxic comments and introduce a beta regression model to do the probabilistic modeling. |
| Outcome: | The proposed model performs well in comprehensive experiments and is scalable. |
Chain-Talker: Chain Understanding and Rendering for Empathetic Conversational Speech Synthesis (2025.findings-acl)
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| Challenge: | Current generative CSS models face interpretability limitations due to insufficient emotional perception and redundant discrete speech coding. |
| Approach: | They propose a framework that aligns synthesized speech with the emotional context of user-agent interactions to achieve empathy. |
| Outcome: | The proposed framework produces more expressive speech than existing methods on three datasets. |
DecorateLM: Data Engineering through Corpus Rating, Tagging, and Editing with Language Models (2024.emnlp-main)
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Ranchi Zhao, Zhen Thai, Yifan Zhang, Shengding Hu, Jie Zhou, Yunqi Ba, Jie Cai, Zhiyuan Liu, Maosong Sun
| Challenge: | Large Language Models (LLMs) are pre-trained on vast datasets composed of billions of tokens harvested from diverse text sources. |
| Approach: | They propose a data engineering method to refine the pretraining corpus through data rating, tagging and editing. |
| Outcome: | The proposed method improves the quality of the pretraining corpus by enhancing 100 billion tokens of the training corpus. |
Can We Steer Reasoning Direction by Thinking Intervention? (2025.findings-emnlp)
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| Challenge: | Large Reason Models suffer from overthinking and erroneous reasoning problems due to the lack of fine-grained control over their reasoning behaviors. |
| Approach: | They propose a paradigm to enable fine-grained control over LRMs’ reasoning behaviors by aligning reasoning trajectories with specific cognitive patterns. |
| Outcome: | The proposed paradigm achieves integration intervention throughout model reasoning processes. |
Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense (2022.findings-acl)
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| Challenge: | ANTHRO extracts over 600K human-written text perturbations and leverages them for realistic adversarial attacks. |
| Approach: | They propose an adversarial text manipulation algorithm that inductively extracts over 600K human-written text perturbations and leverages them for realistic adversarials. |
| Outcome: | The proposed algorithm outperforms the TextBugger baseline with an increase of 50% and 40% in terms of semantic preservation and stealthiness when evaluated by layperson and professional human workers. |
LoRaDA: Low-Rank Direct Attention Adaptation for Efficient LLM Fine-tuning (2025.findings-emnlp)
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| Challenge: | Recent advances in parameter-efficient fine-tuning techniques allow for adjustments to only a minor fraction of the parameters of language models. |
| Approach: | They propose a low-rank direct attention adapted method for efficient LLM fine-tuning . they propose LMAM, which can bring negative attention to self-attention modules . |
| Outcome: | The proposed method outperforms the full fine-tuning method by 2.1% on GLUE benchmark. |
Optimizing Native Sparse Attention with Latent Attention and Local Global Alternating Strategies (2026.findings-acl)
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| Challenge: | Existing research has proposed a variety of training-free and post-training methods for selecting critical key-value pairs at each generation step. |
| Approach: | They propose to use local (sliding-window) and global (compression/selective) attention across layers to enlarge long-context modeling. |
| Outcome: | Experiments on models from 340M to 1.3B parameters show that the proposed method matches or exceeds full attention and native sparse attention in both common-sense reasoning and long-context understanding tasks. |
HABERTOR: An Efficient and Effective Deep Hatespeech Detector (2020.emnlp-main)
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| Challenge: | HABERTOR model is a highly efficient and effective alternative to BERT for the hatespeech classification task. |
| Approach: | They propose to modify BERT's HABERTOR model to generate its own vocabularies and pre-trained it using the largest scale hatespeech dataset. |
| Outcome: | The proposed model is faster, more efficient and more robust than existing methods for hatespeech classification. |
Won’t Get Fooled Again: Answering Questions with False Premises (2023.acl-long)
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| Challenge: | Pre-trained language models (PLMs) are often easily deceived by tricky questions such as “How many eyes does the sun have?” . |
| Approach: | They annotate a FalseQA dataset containing 2365 human-written FPQs and find that PLMs are capable of discriminating FPqs by fine-tuning on moderate numbers. |
| Outcome: | The proposed model can discriminate on FPQs by fine-tuning on moderate numbers of examples and generate reasonable explanations for false premise questions. |
CFlowPsyD: An Analysis-Enhanced Dataset for Asynchronous Psychological Counseling through Self-Optimizing Multi-Agent Framework (2026.findings-acl)
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| Challenge: | Asynchronous psychological counseling (APC) is a crucial mental health service modality that transcends temporal and spatial constraints. |
| Approach: | They propose a self-optimizing multi-agent framework for counseling dialogue generation, CFlowPsy, which utilizes real anonymized counseling cases as seed data to synthesize diverse problem-solving-oriented APC conversations through large language models. |
| Outcome: | The proposed framework synthesizes diverse problem-solving-oriented APC conversations through large language models. |
Evaluating the Expressive Appropriateness of Speech in Rich Contexts (2026.acl-long)
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Tianrui Wang, Ziyang Ma, Yizhou Peng, Haoyu Wang, Zhikang Niu, Zikang Huang, Yihao Wu, Yi-Wen Chao, Yu Jiang, Yuheng Lu, Guanrou Yang, Xuanchen Li, Hexin Liu, Chunyu Qiang, Cheng Gong, Yifan Yang, Tianchi Liu, Junyu Wang, Nana Hou, Meng Ge, Fuming You, Yang Wei, Zhongqian Sun, Hu Haifeng, Xiaobao Wang, Eng Siong Chng, Xie Chen, Longbiao Wang, Jianwu Dang
| Challenge: | Existing methods for evaluating expressive speech focus on word accuracy, naturalness, signal quality, or emotional intensity at the utterance level. |
| Approach: | They propose a framework for Evaluating Expressive Appropriateness in speech that assesses whether a speech sample aligns with the underlying communicative intent implied by its discourse-level narrative context. |
| Outcome: | The proposed framework outperforms existing speech evaluation and analysis systems on a human-annotated test set. |
TinyAlign: Boosting Lightweight Vision-Language Models by Mitigating Modal Alignment Bottlenecks (2026.findings-acl)
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Yuanze Hu, Xinyu Wang, Zhichao Yang, Gen Li, Ye Qiu, Zhaoxin Fan, Yifan Sun, Wenjun wu, Jin Dong, Xiaotie Deng
| Challenge: | Lightweight Vision-Language Models (VLMs) are indispensable for resource-constrained applications. |
| Approach: | They propose a framework that retrieves context from a memory bank to enhance alignment . they propose EMI-based approach to align vision and language models . |
| Outcome: | The proposed framework reduces training loss, accelerates convergence, and enhances task performance with negligible computational overhead. |
Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest (2022.coling-1)
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| Challenge: | Medical relation extraction (MRE) tasks aims to extract relations between entities in medical literature. |
| Approach: | They propose to combine semantic and syntactic information from medical texts by using causal explanation theory. |
| Outcome: | Empirically, the proposed model outperforms existing methods on benchmark medical datasets. |