Papers by Ziyu Yang
Feature Extraction and Steering for Enhanced Chain-of-Thought Reasoning in Language Models (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) can solve reasoning and mathematical problems using the Chain-of-Thought technique, but require costly and long CoT data and fine-tuning. |
| Approach: | They propose a method that uses Sparse Autoencoders to extract interpretable features from vanilla CoT and use them to steer the LLM's internal states. |
| Outcome: | The proposed method uses Sparse Autoencoders (SAEs) to extract interpretable features from vanilla CoT and steer the LLM's internal states during generation. |
Progra: Progress-Aware Reinforcement Learning for Multi-Turn Function Calling (2026.findings-acl)
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Huacan Chai, Zijie Cao, Maolin Ran, Yingxuan Yang, Jianghao Lin, Xin Peng, Hairui Wang, Renjie Ding, Ziyu Wan, Muning Wen, Weiwen Liu, Weinan Zhang, Fei Huang, Ying Wen
| Challenge: | Existing methods for multi-turn function calling are limited by redundancy and lack explicit integration of progress awareness into training. |
| Approach: | They propose a framework that explicitly integrates progress awareness into LLM training for multi-turn function calling. |
| Outcome: | Empirical results show that Progra outperforms existing methods on two public benchmarks. |
Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs (2025.findings-emnlp)
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| Challenge: | Sparse Mixture-of-Experts (SMoE) architectures require loading all expert parameters . previous work focused on expert pruning and merging but focused on neuron-level structure . |
| Approach: | They propose a task-agnostic framework for expert pruning and reconstruction . it prunes redundant experts using router statistics, then decomposes them into neuron-level expert segments . |
| Outcome: | The proposed framework reduces the number of experts and memory usage, making it easier to deploy. |
LoraRetriever: Input-Aware LoRA Retrieval and Composition for Mixed Tasks in the Wild (2024.findings-acl)
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| Challenge: | Low-Rank Adaptation (LoRA) is an effective yet efficient solution for fine-tuning large language models. |
| Approach: | They propose a low-rank Adaptation framework that retrieves and composes multiple LoRAs according to input prompts. |
| Outcome: | Experimental results show that LoraRetriever outperforms baselines in terms of performance and versatility. |
CoEvolve: Training LLM Agents via Agent-Data Mutual Evolution (2026.acl-long)
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| Challenge: | Extensive experiments on AppWorld and BFCL demonstrate consistent and significant improvements over strong base models, yielding absolute gains of 19.43%, 15.58%, and 18.14%, respectively. |
| Approach: | They propose a framework that extracts feedback signals such as forgetting and uncertainty from rollout trajectories and utilizes them to guide LLM-based task synthesis. |
| Outcome: | Extensive experiments on AppWorld and BFCL show that the proposed framework improves over strong base models. |
Two-Pronged Human Evaluation of ChatGPT Self-Correction in Radiology Report Simplification (2024.findings-acl)
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| Challenge: | Radiology reports are highly technical documents aimed primarily at doctor-doctor communication. |
| Approach: | They propose a new evaluation protocol that employs radiologists and laypeople to produce high-quality simplifications. |
| Outcome: | The proposed evaluation protocol combines radiologists and laypeople to produce high-quality simplifications. |
LaoPLM: Pre-trained Language Models for Lao (2022.lrec-1)
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| Challenge: | Pre-trained language models (PLMs) can capture different levels of concepts in context . previous work on Lao has been hampered by the lack of annotated datasets . |
| Approach: | They construct a text classification dataset to alleviate the resource-scarce situation of Lao . they evaluate them on two downstream tasks: part-of-speech tagging and text classification . |
| Outcome: | The proposed model can capture different levels of concepts in context and generate universal language representations. |
H-LegalKI: A Hierarchical Legal Knowledge Integration Framework for Legal Community Question Answering (2024.findings-emnlp)
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| Challenge: | Legal question answering (LQA) aims to bridge the gap between limited availability of legal professionals and the extensive volume of legal issues. |
| Approach: | They propose a legal knowledge retriever and a hierarchical legal knowledge integration framework to address multiple user-specific circumstances. |
| Outcome: | The proposed framework outperforms baselines on the legal community question-answering dataset. |
Data Augmentation for Radiology Report Simplification (2023.findings-eacl)
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| Challenge: | Existing approaches to improve radiology reports are limited due to the high cost of manual simplification. |
| Approach: | They propose a data augmentation approach to generate simplifications of unlabeled radiology sentences using a pre-trained language model and paraphrasing of labeled radiologists sentences. |
| Outcome: | The proposed model generates simplifications of unlabeled radiology sentences and paraphrases labeled radiologists sentences. |
MidPO: Dual Preference Optimization for Safety and Helpfulness in Large Language Models via a Mixture of Experts Framework (2025.findings-emnlp)
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| Challenge: | Recent studies address safety-constrained online and offline preferences optimizations, but offline methods perform poorly in adaptively balancing safety and helpfulness. |
| Approach: | They propose a mixture of experts framework for safety-helpfulness dual Preference Optimization . they combine a single-preference enhanced direct preference optimization approach with a dynamic routing mechanism . |
| Outcome: | The proposed framework outperforms state-of-the-art methods in safety and helpfulness. |
OS Agents: A Survey on MLLM-based Agents for Computer, Phone and Browser Use (2025.acl-long)
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Xueyu Hu, Tao Xiong, Biao Yi, Zishu Wei, Ruixuan Xiao, Yurun Chen, Jiasheng Ye, Meiling Tao, Xiangxin Zhou, Ziyu Zhao, Yuhuai Li, Shengze Xu, Shenzhi Wang, Xinchen Xu, Shuofei Qiao, Zhaokai Wang, Kun Kuang, Tieyong Zeng, Liang Wang, Jiwei Li, Yuchen Eleanor Jiang, Wangchunshu Zhou, Guoyin Wang, Keting Yin, Zhou Zhao, Hongxia Yang, Fan Wu, Shengyu Zhang, Fei Wu
| Challenge: | a new generation of (M)LLMs is enabling the creation of superintelligent AI assistants . OS Agents can complete tasks autonomously and have the potential to significantly enhance the lives of billions of users worldwide. |
| Approach: | They propose to build OS Agents that operate within operating systems' GUIs and GUIs . they examine evaluation metrics and benchmarks to identify promising directions . |
| Outcome: | The proposed agents are based on operating systems (OS) and operating systems frameworks. |