93 citations · 96 across the 19 of their papers we have counts for
20 papers
Scientific Image Quality Assessment via Multi-modal Retrieval-Augmented Generation
Yinuo Zhang, Bingshuo Liu, Zhiying Tu +6
This paper proposes a Retrieval-Augmented Generation (RAG) framework for scientific image quality assessment, designed to simultaneously address both the understanding track (SIQA-…
Defense-as-Skill: Evolving Runtime Guard Skill for Skill-Augmented Agents
Xiaofang Yang, Ziqi Miao, Chen Qian +3
Skill-augmented agents load reusable skills as persistent runtime context, improving task performance but also giving malicious skills a durable channel for steering future actions…
GARDRec: Decision-Level Graph Grounding for Large Language Model Recommendation
Yong Wang, Hongliang Sun, Jinlan Liu +4
Large language models (LLMs) offer new opportunities for recommendation by interpreting item descriptions, user instructions, and external knowledge through natural-language prompt…
Dual-Path LLM Reasoning for Multimodal Few-Shot Knowledge Graph Completion
Jinlan Liu, Zhiying Tu, Yongchao Xing +5
Knowledge graph completion (KGC) aims to infer missing facts in knowledge graphs (KGs), thereby improving their completeness and supporting downstream intelligent applications. How…
SkillWiki: A Living Knowledge Infrastructure for Agent Skills
Dingcheng Huang, Yuda Ding, Bingshuo Liu +8
While knowledge is managed through Wikipedia and software through GitHub, agent skills still lack an infrastructure for large-scale production, governance, and evolution. SkillWiki…
Enhancing the Code Reasoning Capabilities of LLMs via Consistency-based Reinforcement Learning
Zhanyue Qin, Jia Feng, Yibo Lyu +4
Code reasoning refers to the task of predicting the output of a program given its source code and specific inputs. It can measure the reasoning capability of large language models…
Beyond Confidence: The Rhythms of Reasoning in Generative Models
Deyuan Liu, Zecheng Wang, Zhanyue Qin +3
Large Language Models (LLMs) exhibit impressive capabilities yet suffer from sensitivity to slight input context variations, hampering reliability. Conventional metrics like accura…
EchoReview: Learning Peer Review from the Echoes of Scientific Citations
Yinuo Zhang, Dingcheng Huang, Haifeng Suo +9
As the volume of scientific submissions continues to grow rapidly, traditional peer review systems are facing unprecedented scalability pressures, highlighting the urgent need for…
ScEdit: Script-based Assessment of Knowledge Editing
Xinye Li, Zunwen Zheng, Qian Zhang +8
Knowledge Editing (KE) has gained increasing attention, yet current KE tasks remain relatively simple. Under current evaluation frameworks, many editing methods achieve exceptional…
LLMSR@XLLM25: An Empirical Study of LLM for Structural Reasoning
Xinye Li, Mingqi Wan, Dianbo Sui
We present Team asdfo123's submission to the LLMSR@XLLM25 shared task, which evaluates large language models on producing fine-grained, controllable, and interpretable reasoning pr…
Mitigating Gender Bias in Code Large Language Models via Model Editing
Zhanyue Qin, Haochuan Wang, Zecheng Wang +6
In recent years, with the maturation of large language model (LLM) technology and the emergence of high-quality programming code datasets, researchers have become increasingly conf…
Plug-and-Play Performance Estimation for LLM Services without Relying on Labeled Data
Can Wang, Dianbo Sui, Hongliang Sun +3
Large Language Model (LLM) services exhibit impressive capability on unlearned tasks leveraging only a few examples by in-context learning (ICL). However, the success of ICL varies…
HBot: A Chatbot for Healthcare Applications in Traditional Chinese Medicine Based on Human Body 3D Visualization
Bolin Zhang, Zhiwei Yi, Jiahao Wang +3
The unique diagnosis and treatment techniques and remarkable clinical efficacy of traditional Chinese medicine (TCM) make it play an important role in the field of elderly care and…
To Forget or Not? Towards Practical Knowledge Unlearning for Large Language Models
Bozhong Tian, Xiaozhuan Liang, Siyuan Cheng +6
Large Language Models (LLMs) trained on extensive corpora inevitably retain sensitive data, such as personal privacy information and copyrighted material. Recent advancements in kn…
UNO Arena for Evaluating Sequential Decision-Making Capability of Large Language Models
Zhanyue Qin, Haochuan Wang, Deyuan Liu +9
Sequential decision-making refers to algorithms that take into account the dynamics of the environment, where early decisions affect subsequent decisions. With large language model…
Pruning via Merging: Compressing LLMs via Manifold Alignment Based Layer Merging
Deyuan Liu, Zhanyue Qin, Hairu Wang +12
While large language models (LLMs) excel in many domains, their complexity and scale challenge deployment in resource-limited environments. Current compression techniques, such as…
Checkpoint Merging via Bayesian Optimization in LLM Pretraining
Deyuan Liu, Zecheng Wang, Bingning Wang +6
The rapid proliferation of large language models (LLMs) such as GPT-4 and Gemini underscores the intense demand for resources during their training processes, posing significant ch…
A Framework for Effective Invocation Methods of Various LLM Services
Can Wang, Dianbo Sui, Bolin Zhang +4
Large Language Models (LLMs) have shown impressive abilities in solving various natural language processing tasks and are now widely offered as services. LLM services enable users…
Economics Arena for Large Language Models
Shangmin Guo, Haoran Bu, Haochuan Wang +4
Large language models (LLMs) have been extensively used as the backbones for general-purpose agents, and some economics literature suggest that LLMs are capable of playing various…
Woodpecker: Hallucination Correction for Multimodal Large Language Models
Shukang Yin, Chaoyou Fu, Sirui Zhao +7
Hallucination is a big shadow hanging over the rapidly evolving Multimodal Large Language Models (MLLMs), referring to the phenomenon that the generated text is inconsistent with t…