Papers by Hongyi Yuan
Text Diffusion Model with Encoder-Decoder Transformers for Sequence-to-Sequence Generation (2024.naacl-long)
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| Challenge: | Existing diffusion models are applied to continuous feature space while texts are sequences of discrete categorical tokens. |
| Approach: | They propose to use an encoder-decoder Transformer architecture to approach sequence-to-sequence text generation. |
| Outcome: | The proposed model improves on five sequence-to-sequence generation tasks compared to other diffusion-based models regarding text quality and inference time. |
SHAPE: Unifying Safety, Helpfulness and Pedagogy for Educational LLMs (2026.acl-long)
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| Challenge: | Existing educational LLMs are vulnerable to pedagogical jailbreaks where students use answer-inducing prompts to elicit solutions rather than scaffolded instructions. |
| Approach: | They propose a graph-augmented tutoring pipeline that infers prerequisite concepts from queries and identifies mastery gaps. |
| Outcome: | The proposed method improves safety under two pedagogical jailbreak scenarios while maintaining near-ceiling helpfulness under the same evaluation protocol. |
Generative Biomedical Entity Linking via Knowledge Base-Guided Pre-training and Synonyms-Aware Fine-tuning (2022.naacl-main)
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| Challenge: | Generative methods for biomedical entity linking (EL) use synonyms knowledge from knowledge bases (KB) this is not trivial to inject into a generative method, but it is cost-effective. |
| Approach: | They propose to inject synonyms knowledge into a generative model of biomedical EL by constructing synthetic samples with synonyms and definitions from KB and requiring the model to recover concept names. |
| Outcome: | The proposed method achieves state-of-the-art results on several biomedical EL tasks without candidate selection. |
KV Cache Compression, But What Must We Give in Return? A Comprehensive Benchmark of Long Context Capable Approaches (2024.findings-emnlp)
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Jiayi Yuan, Hongyi Liu, Shaochen Zhong, Yu-Neng Chuang, Songchen Li, Guanchu Wang, Duy Le, Hongye Jin, Vipin Chaudhary, Zhaozhuo Xu, Zirui Liu, Xia Hu
| Challenge: | Long context capability is a crucial competency for large language models as it mitigates the human struggle to digest long-form texts. |
| Approach: | They propose to evaluate 10+ state-of-the-art approaches for long context-capable LLMs. |
| Outcome: | The proposed methods are compared against 10+ state-of-the-art approaches across seven categories of long context tasks. |
Rubrik’s Cube: Testing a New Rubric for Evaluating Explanations on the CUBE dataset (2025.acl-long)
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Diana Galvan-Sosa, Gabrielle Gaudeau, Pride Kavumba, Yunmeng Li, Hongyi Gu, Zheng Yuan, Keisuke Sakaguchi, Paula Buttery
| Challenge: | Large-Language Models (LLMs) are increasingly being used in explanation generation tasks due to their unreliability. |
| Approach: | They propose a rubric and a dataset of 26k explanations written and quality-annotated using the rubric by humans and six open- and closed-source LLMs to test their proposed rubric. |
| Outcome: | The proposed rubric and CUBE dataset focuses on reasoning and language tasks and provides the necessary diversity to test it. |
Speculative Contrastive Decoding (2024.acl-short)
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| Challenge: | Large language models (LLMs) exhibit exceptional performance in language tasks, yet their auto-regressive inference is limited due to high computational requirements and is sub-optimal due to the exposure bias. |
| Approach: | They propose a decoding approach that leverages predictions from smaller language models to achieve both decoding acceleration and quality improvement. |
| Outcome: | The proposed method achieves both decoding acceleration and quality improvement on four diverse language tasks. |
MuggleMath: Assessing the Impact of Query and Response Augmentation on Math Reasoning (2024.acl-long)
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Chengpeng Li, Zheng Yuan, Hongyi Yuan, Guanting Dong, Keming Lu, Jiancan Wu, Chuanqi Tan, Xiang Wang, Chang Zhou
| Challenge: | In math reasoning with large language models, fine-tuning data augmentation by query evolution and diverse reasoning paths is empirically verified effective. |
| Approach: | They propose to fine-tune data augmentation by query evolution and diverse reasoning paths. |
| Outcome: | The proposed model achieves new state-of-the-art on GSM8K and MATH. |
HyPe: Better Pre-trained Language Model Fine-tuning with Hidden Representation Perturbation (2023.acl-long)
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| Challenge: | Existing techniques to fine-tune pre-trained language models on downstream tasks are inadequate. |
| Approach: | They propose a technique to perturb hidden Transformers representations by enhancing generalization of hidden representations from different layers. |
| Outcome: | The proposed technique outperforms vanilla fine-tuning and enhances generalization of hidden representations from different layers. |
How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition (2024.acl-long)
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Guanting Dong, Hongyi Yuan, Keming Lu, Chengpeng Li, Mingfeng Xue, Dayiheng Liu, Wei Wang, Zheng Yuan, Chang Zhou, Jingren Zhou
| Challenge: | supervised fine-tuning (SFT) is a technique used to enhance multiple abilities in large language models. |
| Approach: | They propose to study the interplay of data composition between mathematical reasoning, code generation, and general human-aligning abilities during supervised fine-tuning. |
| Outcome: | The proposed model improves math reasoning and code generation with increasing data amount . the proposed model size and SFT strategies can be used to learn multiple skills with different scaling patterns. |
LoRATK: LoRA Once, Backdoor Everywhere in the Share-and-Play Ecosystem (2025.findings-emnlp)
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Hongyi Liu, Shaochen Zhong, Xintong Sun, Minghao Tian, Mohsen Hariri, Zirui Liu, Ruixiang Tang, Zhimeng Jiang, Jiayi Yuan, Yu-Neng Chuang, Li Li, Soo-Hyun Choi, Rui Chen, Vipin Chaudhary, Xia Hu
| Challenge: | distributing LLMs without a proven track record like ‘meta-llama‘ or ‘qwen‘ rarely gains community traction. |
| Approach: | They propose a simple, efficient, yet specific recipe for a backdoor LoRA to be injected into task-enhancing LoRAs and examine the mechanisms of such infections. |
| Outcome: | The proposed model allows attackers to scale the distribution of compromised LoRAs with minimal effort by leveraging the rich pool of shared LoRA assets. |
Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models (2024.naacl-long)
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| Challenge: | Existing ensemble methods for Large Language Models focus on reward model ranking of outputs, leading to significant computation overhead. |
| Approach: | They propose a reward-guided routing method distilling rewards on training queries to train a routing function. |
| Outcome: | The proposed method outperforms the best single model and ranks first on 44% of tasks. |
AutoL2S: Auto Long-Short Reasoning for Efficient Large Language Models (2026.findings-acl)
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Feng Luo, Yu-Neng Chuang, Guanchu Wang, Hoang Anh Duy Le, Shaochen Zhong, Hongyi Liu, Jiayi Yuan, Yang Sui, Vladimir Braverman, Vipin Chaudhary, Xia Hu
| Challenge: | Existing approaches to distilling large language models (LLMs) are inefficient and generate excessively long chain-of-thought reasoning even for inputs that admit concise solutions. |
| Approach: | They propose a distillation framework that empowers non-reasoning LLMs to think only when necessary. |
| Outcome: | The proposed framework reduces reasoning length up to 71% with minimal accuracy loss while preserving accuracy. |