Papers by Dongsheng Luo
PEPE: Long-context Extension for Large Language Models via Periodic Extrapolation Positional Encodings (2025.findings-emnlp)
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Jikun Hu, Dongsheng Guo, Yuli Liu, Qingyao Ai, Lixuan Wang, Xuebing Sun, Qilei Zhang, Quan Zhou, Cheng Luo
| Challenge: | Long-context extension attempts to extend contextual window in pre-trained LLMs . primary method involves expanding initial positional encodings, disrupting positional learning . |
| Approach: | They propose a new extension strategy based on Rotary Position Embedding to extend contextual window in pre-trained large language models. |
| Outcome: | The proposed method can extend the contextual window in pre-trained large language models . expansion disrupts positional encodings learned during pre-training, authors show . |
Mitigate Position Bias in LLMs via Scaling a Single Hidden States Channel (2025.findings-acl)
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Yijiong Yu, Huiqiang Jiang, Xufang Luo, Qianhui Wu, Chin-Yew Lin, Dongsheng Li, Yuqing Yang, Yongfeng Huang, Lili Qiu
| Challenge: | Long-context language models exhibit position bias, also known as "lost in the middle" research shows that even long-contemporary LLMs fail to utilize all context information effectively . |
| Approach: | They propose a method to mitigate position bias by scaling positional hidden states . they propose to use a channel of hidden states to modify positional Hidden states a LCLM's positional bias . |
| Outcome: | The proposed method can improve performance by 15.2% in a "lost in the middle" benchmark. |
Exploring Multi-Modal Data with Tool-Augmented LLM Agents for Precise Causal Discovery (2025.findings-acl)
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| Challenge: | Existing statistical causal discovery methods rely on observational data and often overlook the semantic cues inherent in cause-and-effect relationships. |
| Approach: | They propose a multi-agent system powered by tool-augmented Large Language Models that can combine data from multiple modalities and integrate multi-modal data for knowledge-driven reasoning. |
| Outcome: | The proposed system has two agents: a Data Augmentation agent that retrieves and processes modality-augmented data, and a Causal Constraint agent that integrates multi-modal data for knowledge-driven reasoning. |
MedPlan: A Two-Stage RAG-Based System for Personalized Medical Plan Generation (2025.acl-industry)
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Hsin-Ling Hsu, Cong-Tinh Dao, Luning Wang, Zitao Shuai, Thao Nguyen Minh Phan, Jun-En Ding, Chun-Chieh Liao, Pengfei Hu, Xiaoxue Han, Chih-Ho Hsu, Dongsheng Luo, Wen-Chih Peng, Feng Liu, Fang-Ming Hung, Chenwei Wu
| Challenge: | Existing systems focus primarily on assessment rather than treatment planning. |
| Approach: | They propose a framework that structures LLM reasoning to align with real-life workflows. |
| Outcome: | The proposed framework outperforms baseline approaches in assessment accuracy and treatment plan quality. |
LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression (2024.acl-long)
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| Challenge: | Longer prompts introduce irrelevant and redundant information, which can weaken LLMs' performance. |
| Approach: | They propose a prompt compression tool that improves LLMs' perception of key information in input prompts by up to 21.4% with around 4x fewer tokens in GPT-3.5-Turbo. |
| Outcome: | The proposed solution improves performance and reduces costs and latency by up to 21.4% with around 4x fewer tokens in the NaturalQuestions benchmark. |