Papers by Yao Fu
LIFTED: Multimodal Clinical Trial Outcome Prediction via Large Language Models and Mixture-of-Experts (2025.findings-emnlp)
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| Challenge: | Clinical trials are costly and pivotal processes that require substantial expenses . a new approach to integrate multimodal data for clinical outcome prediction is needed . |
| Approach: | a proposed framework transforms modality-specific data into natural language descriptions . a sparse Mixture-of-Experts mechanism then identifies shared patterns across modalities . |
| Outcome: | a proposed framework outperforms baseline methods in predicting clinical trial outcomes . it transforms modality-specific data into natural language descriptions, encoded via unified encoders . |
Natural Answer Generation with Heterogeneous Memory (N18-1)
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| Challenge: | Recent work on memory augmented encoder-decoder frameworks has shown promising progress for natural language generation tasks. |
| Approach: | They propose a memory-augmented encoder-decoder framework that takes care of memory contents from different sources to explicitly avoid repetition. |
| Outcome: | The proposed approach can produce readable and meaningful answer sentences while maintaining high coverage for given answer information. |
Beyond Blind Following: Evaluating Robustness of LLM Agents under Imperfect Guidance (2026.eacl-long)
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Yao Fu, Ran Qiu, Xinhe Wang, Jacob Sansom, Sathvika Ayyappa Prabhu, Huijie Tang, Jaekyeom Kim, Sungryull Sohn, Honglak Lee
| Challenge: | Large language models (LLMs) have shown strong capabilities as task-solving agents across interactive domains, but in complex environments, auxiliary guidance may be imperfect. |
| Approach: | They propose a benchmark to measure the robustness of large language models under imperfect guidance. |
| Outcome: | The proposed benchmark compared LLM agents in navigation, cooking, and gaming in a variety of environments with auxiliary guidance and noisy or underspecified instructions extracted from demonstrations. |
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. |
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. |
Few-shot Subgoal Planning with Language Models (2022.naacl-main)
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| Challenge: | Pre-trained language models have shown successful progress in many text understanding benchmarks. |
| Approach: | They propose a strategy to re-rank language model predictions based on interaction and feedback from the environment. |
| Outcome: | The proposed approach shows competitive performance on subgoal prediction and task completion in the ALFRED benchmark compared to prior methods that assume more subgoals supervision. |
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. |
FAEDKV: Infinite-Window Fourier Transform for Unbiased KV Cache Compression (2025.findings-emnlp)
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| Challenge: | Current compression strategies, including token eviction and learned projections, often lead to biased representations and may require costly model retraining. |
| Approach: | They propose a training-free KV cache compression framework that equalizes the contribution of all tokens to the compressed representation. |
| Outcome: | The proposed framework ensures unbiased information retention in the KV cache. |
Interactive and Expressive Code-Augmented Planning with Large Language Models (2025.acl-long)
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Anthony Zhe Liu, Xinhe Wang, Jacob Sansom, Yao Fu, Jongwook Choi, Sungryull Sohn, Jaekyeom Kim, Honglak Lee
| Challenge: | Large Language Models (LLMs) have strong abilities in common-sense reasoning and interactive decision-making, but struggle with complex, long-horizon planning tasks. |
| Approach: | They propose a code-based LLM planning approach that is code-expressive while also dynamically adapting from errors. |
| Outcome: | The proposed approach can be error-prone and insufficient for handling ambiguous or unstructured data. |
How to Make LMs Strong Node Classifiers? (2026.findings-eacl)
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Zhe Xu, Kaveh Hassani, Si Zhang, Hanqing Zeng, Michihiro Yasunaga, Limei Wang, Dongqi Fu, Ning Yao, Bo Long, Hanghang Tong
| Challenge: | Language Models (LMs) are increasingly challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers (GTs). |
| Approach: | They propose a novel approach that empowers off-the-shelf LMs to achieve performance comparable to state-of-the art (SOTA) GNNs on node classification tasks without requiring any architectural modifications. |
| Outcome: | The proposed approach outperforms existing GNNs on node classification tasks and is open-source upon publication. |
On Orthogonality Constraints for Transformers (2021.acl-short)
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Aston Zhang, Alvin Chan, Yi Tay, Jie Fu, Shuohang Wang, Shuai Zhang, Huajie Shao, Shuochao Yao, Roy Ka-Wei Lee
| Challenge: | a dedicated study on orthogonality constraints for transformers has been lacking . plug-and-play constraints increase the BLEU of transformers . |
| Approach: | They propose to use plug-and-play constraints to encourage matrices to be orthogonal for numerical stability. |
| Outcome: | The proposed constraint increases the BLEU on the large-scale WMT’16 EnDe benchmark by a factor of 28.4 to 29.6. |
CMDAG: A Chinese Metaphor Dataset with Annotated Grounds as CoT for Boosting Metaphor Generation (2024.lrec-main)
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| Challenge: | Metaphors are a prominent linguistic device in human language and literature, as they add color, imagery, and emphasis to enhance effective communication. |
| Approach: | They propose a large-scale high quality annotated Chinese Metaphor Corpus . they use a set of guidelines to ensure the accuracy and consistency of their annotations . |
| Outcome: | The proposed corpus generates metaphors that resonate more with real-world intuition. |
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. |
LLM-as-a-Coauthor: Can Mixed Human-Written and Machine-Generated Text Be Detected? (2024.findings-naacl)
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Qihui Zhang, Chujie Gao, Dongping Chen, Yue Huang, Yixin Huang, Zhenyang Sun, Shilin Zhang, Weiye Li, Zhengyan Fu, Yao Wan, Lichao Sun
| Challenge: | Current research focuses on purely MGT detection without adequately addressing mixed scenarios including AI-revised Human-Written Text (HWT) and human-revealed MGT. |
| Approach: | They define mixtext, a form of mixed text involving both AI and human-generated content, and then use a MixSet dataset to assess their effectiveness. |
| Outcome: | The proposed detectors struggle to identify mixtext, particularly in dealing with subtle modifications and style adaptability. |
Noisy-Labeled NER with Confidence Estimation (2021.naacl-main)
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| Challenge: | Recent studies in deep learning have shown significant progress in named entity recognition (NER) . however, most existing works assume clean data annotation, while real-world data typically involve a large amount of noises. |
| Approach: | They propose a confidence estimation approach for named entity recognition using noisy labels using local and global independence assumptions. |
| Outcome: | The proposed method marginalizes out labels of low confidence with a CRF model and integrates it into a self-training framework for boosting performance. |
Digest the Knowledge: Large Language Models empowered Message Passing for Knowledge Graph Question Answering (2025.acl-long)
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| Challenge: | Existing methods to augment large language models (LLMs) with external knowledge are unorganized and unorganized. |
| Approach: | They propose a method that learns a concise facts graph and encodes it into multi-level lists of texts to augment LLMs. |
| Outcome: | The proposed method improves on all 5 knowledge graph question answering datasets and offers human-level semantic explainability. |
MedCOD: Enhancing English-to-Spanish Medical Translation of Large Language Models Using Enriched Chain-of-Dictionary Framework (2025.findings-emnlp)
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| Challenge: | MedCOD integrates domain-specific structured knowledge into large language models . evaluators evaluated four open-source LLMs with structured prompts . |
| Approach: | They propose a framework that integrates domain-specific structured knowledge into large language models . they constructed a parallel corpus of 2,999 English-Spanish MedlinePlus articles . |
| Outcome: | The proposed framework improves translation quality across four open-source LLMs. |
JW-SVD: Bridging the Cross-Modal Mismatch in Post-Training MLLM Compression (2026.acl-long)
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| Challenge: | Existing methods for compression of Multimodal Large Language Models lack multimodal adaptation to preserve cross-modal synergy. |
| Approach: | They propose a framework that aligns vision and language manifolds via a Joint Covariance basis and propose Global Spectrum-Aware Truncation to dynamically transfer parameter budget to the sensitive Backbone. |
| Outcome: | Experiments on Qwen2.5-VL and Llama-3-Next confirm that JW-SVD retains both text and image capabilities. |
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 . |
Data-to-text Generation with Variational Sequential Planning (2022.tacl-1)
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| Challenge: | Recent advances in data-to-text generation have greatly facilitated the task of generating textual output from non-linguistic input. |
| Approach: | They propose a neural model enhanced with a planning component responsible for organizing high-level information in a coherent and meaningful way. |
| Outcome: | The proposed model outperforms baseline models and is sample-efficient in the face of limited training data. |