From the 1 of 7 linked papers with an AI index.
2 citations · 2 across the 2 of their papers we have counts for
9 papers
StructureClaw: Traceable LLM Agents and an Executable Benchmark for Structural Engineering Workflows
Sizhong Qin, Yi Gu, Yao Jiang +13
The paper introduces StructureClaw, a workbench where LLM agents execute structured engineering workflows by managing typed tools and shared artifacts, and presents StructureClaw-B…
ChatSOP: An SOP-Guided MCTS Planning Framework for Controllable LLM Dialogue Agents
Zhigen Li, Jianxiang Peng, Yanmeng Wang +13
Dialogue agents powered by Large Language Models (LLMs) show superior performance in various tasks. Despite the better user understanding and human-like responses, their **lack of…
TaP: A Taxonomy-Guided Framework for Automated and Scalable Preference Data Generation
Renren Jin, Tianhao Shen, Xinwei Wu +9
Conducting supervised and preference fine-tuning of large language models (LLMs) requires high-quality datasets to improve their ability to follow instructions and align with human…
OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement
Tianyu Zheng, Ge Zhang, Tianhao Shen +5
The introduction of large language models has significantly advanced code generation. However, open-source models often lack the execution capabilities and iterative refinement of…
Large Language Model Safety: A Holistic Survey
Dan Shi, Tianhao Shen, Yufei Huang +10
The rapid development and deployment of large language models (LLMs) have introduced a new frontier in artificial intelligence, marked by unprecedented capabilities in natural lang…
Automated Progressive Red Teaming
Bojian Jiang, Yi Jing, Tianhao Shen +3
Ensuring the safety of large language models (LLMs) is paramount, yet identifying potential vulnerabilities is challenging. While manual red teaming is effective, it is time-consum…