Papers by Yufei Gao
DRBO: Mitigating Short Board Effect via Dynamic Reward Balancing in Multi-reward LLM Optimization (2025.findings-emnlp)
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| Challenge: | a new framework to optimize large language models (LLMs) for evaluation metrics is needed to balance weaker metrics. |
| Approach: | They propose a Dynamic Reward Balancing Optimization framework to mitigate the "short-board effect" they apply it to single-task and multi-type task scenarios . |
| Outcome: | The proposed framework improves performance and balances performance across multiple metrics. |
Self-Renewal Prompt Optimizing with Implicit Reasoning (2024.findings-emnlp)
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Zihan Liang, Ben Chen, Zhuoran Ran, Zihan Wang, Huangyu Dai, Yufei Ma, Dehong Gao, Xiaoyan Cai, Libin Yang
| Challenge: | Recent advances in NLP have been driven by the development of Large Language Models (LLMs). |
| Approach: | They propose a self-renewal approach to optimize LLM outputs to better align with human preferences without supervised fine-tuning. |
| Outcome: | The proposed approach improves outputs to better align with human preferences across LLMs and tasks without supervised fine-tuning. |
Document Segmentation Matters for Retrieval-Augmented Generation (2025.findings-acl)
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Zhitong Wang, Cheng Gao, Chaojun Xiao, Yufei Huang, Shuzheng Si, Kangyang Luo, Yuzhuo Bai, Wenhao Li, Tangjian Duan, Chuancheng Lv, Guoshan Lu, Gang Chen, Fanchao Qi, Maosong Sun
| Challenge: | Existing rule-based chunking methods lead to suboptimal splits, where overly large chunks introduce irrelevant information and small chunks lack semantic coherence. |
| Approach: | They propose a method that leverages document summaries as pseudo-instructions to guide chunking by computing semantic similarity between sentences and the summary. |
| Outcome: | Experiments on multiple open-domain question-answering benchmarks show that PIC significantly improves retrieval accuracy (Hits@k) and end-to-end QA performance (Exact Match) without any additional training. |
MoDULA: Mixture of Domain-Specific and Universal LoRA for Multi-Task Learning (2024.emnlp-main)
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Yufei Ma, Zihan Liang, Huangyu Dai, Ben Chen, Dehong Gao, Zhuoran Ran, Wang Zihan, Linbo Jin, Wen Jiang, Guannan Zhang, Xiaoyan Cai, Libin Yang
| Challenge: | Recent advances in open-source Large Language Models (LLMs) have achieved notable successes in natural language processing. |
| Approach: | They propose a Parameter Efficient Fine-Tuning paradigm for improved fine-tuning and parameter efficiency in multi-task learning. |
| Outcome: | The proposed model outperforms existing methods on multi-task learning while reducing training costs by over 80% without losing general capability. |
White-Box Multi-Objective Adversarial Attack on Dialogue Generation (2023.acl-long)
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| Challenge: | Pre-trained transformers are popular in state-of-the-art dialogue generation systems . however, they are vulnerable to adversarial samples crafted by small and imperceptible perturbations. |
| Approach: | They propose a multi-objective attack method that balances two objectives: generation accuracy and length. |
| Outcome: | The proposed method significantly degrades state-of-the-art DG models with a higher success rate than traditional accuracy-based methods. |
GLTW: Joint Improved Graph Transformer and LLM via Three-Word Language for Knowledge Graph Completion (2025.findings-acl)
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Kangyang Luo, Yuzhuo Bai, Cheng Gao, Shuzheng Si, Zhu Liu, Yingli Shen, Zhitong Wang, Cunliang Kong, Wenhao Li, Yufei Huang, Ye Tian, Xuantang Xiong, Lei Han, Maosong Sun
| Challenge: | Existing knowledge graphs lack the ability to integrate structural information into LLMs and output predictions deterministically. |
| Approach: | They propose a method which encodes structural information of KGs and merges it with LLMs to enhance KGC performance. |
| Outcome: | The proposed method improves the performance of KG Completion datasets on KGs by integrating structural information with LLMs. |
Learning to Edit: Aligning LLMs with Knowledge Editing (2024.acl-long)
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Yuxin Jiang, Yufei Wang, Chuhan Wu, Wanjun Zhong, Xingshan Zeng, Jiahui Gao, Liangyou Li, Xin Jiang, Lifeng Shang, Ruiming Tang, Qun Liu, Wei Wang
| Challenge: | Existing knowledge editing techniques rely on memorizing updated knowledge, impeding LLMs from effectively combining the new knowledge with their inherent knowledge when answering questions. |
| Approach: | They propose a Learning to Edit framework that equips LLMs with the ability to apply updated knowledge to input questions through a two-phase process . |
| Outcome: | The proposed framework outperforms existing methods in knowledge editing tasks and compares it with four benchmarks and two LLM architectures. |
Com2 : A Causal-Guided Benchmark for Exploring Complex Commonsense Reasoning in Large Language Models (2025.acl-long)
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Kai Xiong, Xiao Ding, Yixin Cao, Yuxiong Yan, Li Du, Yufei Zhang, Jinglong Gao, Jiaqian Liu, Bing Qin, Ting Liu
| Challenge: | Existing works focus on complex tasks like math and code, while complex commonsense reasoning remains underexplored due to its uncertainty and lack of structure. |
| Approach: | They propose to build a benchmark for large language models based on complex commonsense reasoning based upon causal event graphs and causal theory. |
| Outcome: | The proposed benchmark combines a complex commonsense reasoning benchmark with a detective story to achieve a more challenging subset. |
Is Your LLM Outdated? A Deep Look at Temporal Generalization (2025.naacl-long)
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| Challenge: | Existing methods to evaluate large language models are limited due to their inherent dynamic nature and the inherent dynamicity of language and information. |
| Approach: | They introduce a new evaluation framework that employs fresh text and event prediction for assessing LLMs’ temporal adaptability. |
| Outcome: | The proposed framework shows significant temporal biases and a decline in performance over time. |