Papers by Xiangyu Shi
Hierarchical Memory Organization for Wikipedia Generation (2025.acl-long)
Copied to clipboard
Eugene J. Yu, Dawei Zhu, Yifan Song, Xiangyu Wong, Jiebin Zhang, Wenxuan Shi, Xiaoguang Li, Qun Liu, Sujian Li
| Challenge: | Existing methods for generating Wikipedia articles do not utilize memory directly for outline generation. |
| Approach: | They propose a method to generate Wikipedia articles autonomously by leveraging a hierarchical memory architecture. |
| Outcome: | The proposed framework outperforms baseline methods in producing informative and reliable articles. |
Large Language Models in Bioinformatics: A Survey (2025.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are revolutionizing bioinformatics, enabling advanced analysis of DNA, RNA, proteins, and single-cell data. |
| Approach: | They examine the evolution of Large Language Models (LLMs) in bioinformatics and precision medicine by focusing on genomic sequence modeling, RNA structure prediction, protein function inference, and single-cell transcriptomics. |
| Outcome: | The proposed models are capable of predicting RNA structure and function and predicting single-cell transcriptomics. |
PsyScore: A Psychometrically-Aware Framework for Trait-Adaptive Essay Scoring and ZPD-Scaffolded Feedback (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to Automated Essay Scoring (AES) treat scoring and feedback as separate components, resulting in fragmentation. |
| Approach: | They propose a psychometrically-aware framework that integrates diagnostic assessment with instructional scaffolding through a shared latent ability representation. |
| Outcome: | The proposed framework integrates diagnostic assessment with instructional scaffolding through a shared latent ability representation. |
Counterfactual Adversarial Learning with Representation Interpolation (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models with statistical bias are prone to memorized correlations . large pre-trained models such as BERT have revolutionized the model development paradigm in natural language processing . |
| Approach: | They propose a framework to tackle the problem from a causal perspective using a latent space interpolation approach. |
| Outcome: | Extensive experiments show that CAT achieves substantial performance improvement over SOTA across different downstream tasks, including sentence classification, natural language inference and question answering. |
LM2Protein: A Structure-to-Token Protein Large Language Model (2025.findings-emnlp)
Copied to clipboard
| Challenge: | RNA-binding proteins are critical for various molecular functions, relying on their precise tertiary structures. |
| Approach: | They propose a method to integrate protein 3D structural data within a sequence processing framework. |
| Outcome: | The proposed method achieves high sequence recovery in inverse folding and protein-conditioned RNA design. |
Logic-Consistency Text Generation from Semantic Parses (2021.findings-acl)
Copied to clipboard
| Challenge: | Text generation from semantic parses is challenging due to the complexity of the inner logic and the lack of automatic evaluation metrics for logic consistency. |
| Approach: | They propose a framework for logic consistent text generation from semantic parses that employs iterative training procedures and quality control. |
| Outcome: | The proposed framework enhances logic consistency and human evaluation on two benchmark datasets. |
Combining Static Word Embeddings and Contextual Representations for Bilingual Lexicon Induction (2021.findings-acl)
Copied to clipboard
| Challenge: | Bilingual Lexicon Induction (BLI) aims to map words in one language to their translations in another. |
| Approach: | They propose a mechanism to combine static word embeddings and contextual representations to utilize the advantages of both paradigms. |
| Outcome: | The proposed method improves performance on supervised and unsupervised BLI benchmarks on all language pairs by average improving 3.2 points over baselines. |
EasyGen: Easing Multimodal Generation with BiDiffuser and LLMs (2024.acl-long)
Copied to clipboard
| Challenge: | Existing multimodal models that depend on encoders like CLIP or ImageBind need ample amounts of training data to bridge modalities. |
| Approach: | They propose an efficient model that leverages bidirectional conditional diffusion model to foster more efficient modality interactions. |
| Outcome: | The proposed model is able to train a projection layer linking an LLM and an adapter to align the LLM’s text space with the bidirectional diffusion model. |
When Helpers Become Hazards: A Benchmark for Analyzing Multimodal LLM-Powered Safety in Daily Life (2026.findings-acl)
Copied to clipboard
Xinyue Lou, Xu Jinan, Jingyi Yin, Xiaolong Wang, Zhaolu Kang, null Liaoyouwei, Yixuan Wang, Xiangyu Shi, Fengran Mo, SU Yao, Kaiyu Huang
| Challenge: | Safety impact of Multimodal Large Language Models (MLLMs) on human behavior is evaluated in this study. |
| Approach: | They propose a safety-warning-based evaluation framework that encourages models to provide clear and informative safety warnings, rather than generic refusals. |
| Outcome: | The proposed safety-warning-based evaluation framework encourages models to provide clear and informative safety warnings, rather than generic refusals. |
RoChBert: Towards Robust BERT Fine-tuning for Chinese (2022.findings-emnlp)
Copied to clipboard
Zihan Zhang, Jinfeng Li, Ning Shi, Bo Yuan, Xiangyu Liu, Rong Zhang, Hui Xue, Donghong Sun, Chao Zhang
| Challenge: | Pre-trained language models (e.g., BERT) have been proved vulnerable to adversarial texts. |
| Approach: | They propose to fuse Chinese phonetic and glyph features into pre-trained models by using a more comprehensive adversarial graph. |
| Outcome: | The proposed framework outperforms existing methods in significant ways on a wide range of tasks while remaining accurate on benign texts. |
Think in Safety: Unveiling and Mitigating Safety Alignment Collapse in Multimodal Large Reasoning Model (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing Large Reasoning Models have demonstrated broad application potential, yet their safety and reliability remain critical concerns. |
| Approach: | They conduct a safety evaluation of 13 MLRMs across 5 benchmarks and examine their safety performance. |
| Outcome: | The proposed model improves safety on jailbreak and safety-awareness benchmarks. |