6 papers · 1 filter
Scaling Agentic Capabilities via Grounded Interaction Synthesis
Wenhang Shi, Jinhao Dong, Yiren Chen +4
General agentic intelligence hinges on the ability to interact with diverse real-world tools to complete complex tasks, a capability fundamentally tied to the quality of interactio…
Training Prompt Matters: State-Adaptive Optimization for Robust Fine-Tuning
Wenhang Shi, Yiren Chen, Shuqing Bian +5
While prompt engineering is instrumental in maximizing the capabilities of Large Language Models (LLMs) during inference, the role of prompts during training remains critically und…
TERGAD: Structure-Aware Text-Enhanced Representations for Graph Anomaly Detection
Wen Shi, Zhe Wang, Huafei Huang +6
Graph Anomaly Detection (GAD) aims to identify atypical graph entities, such as nodes, edges, or substructures, that deviate significantly from the majority. While existing text-ri…
Investigating the Impact of Rationales for LLMs on Natural Language Understanding
Wenhang Shi, Shuqing Bian, Yiren Chen +5
Chain-of-thought (CoT) rationales, which provide step-by-step reasoning to derive final answers, benefit LLMs in both inference and training. Incorporating rationales, either by ge…
No Loss, No Gain: Gated Refinement and Adaptive Compression for Prompt Optimization
Wenhang Shi, Yiren Chen, Shuqing Bian +6
Prompt engineering is crucial for leveraging the full potential of large language models (LLMs). While automatic prompt optimization offers a scalable alternative to costly manual…
Joint Knowledge Editing for Information Enrichment and Probability Promotion
Wenhang Shi, Yiren Chen, Shuqing Bian +5
Knowledge stored in large language models requires timely updates to reflect the dynamic nature of real-world information. To update the knowledge, most knowledge editing methods f…