AgentTuning: Enabling Generalized Agent Abilities for LLMs

Open large language models (LLMs) with great performance in various tasks have significantly advanced the development of LLMs. However, they are far inferior to commercial models such as ChatGPT and GPT-4 when acting as agents to tackle complex tasks in the real world. These agent tasks employ LLMs as the central controller responsible for planning, memorization, and tool utilization, necessitating both fine-grained prompting methods and robust LLMs to achieve satisfactory performance. Though many prompting methods have been proposed to complete particular agent tasks, there is lack of research focusing on improving the agent capabilities of LLMs themselves without compromising their general abilities. In this work, we present AgentTuning, a simple and general method to enhance the agent abilities of LLMs while maintaining their general LLM capabilities. We construct AgentInstruct, a lightweight instruction-tuning dataset containing high-quality interaction trajectories. We employ a hybrid instruction-tuning strategy by combining AgentInstruct with open-source instructions from general domains. AgentTuning is used to instruction-tune the Llama 2 series, resulting in AgentLM. Our evaluations show that AgentTuning enables LLMs' agent capabilities without compromising general abilities. The AgentLM-70B is comparable to GPT-3.5-turbo on unseen agent tasks, demonstrating generalized agent capabilities. We open source the AgentInstruct and AgentLM-7B, 13B, and 70B models at https://github.com/THUDM/AgentTuning, serving open and powerful alternatives to commercial LLMs for agent tasks.Comment: 31 page

Expanded CURB-65: A new score system predicts severity of community-acquired pneumonia with superior efficiency

Aim of this study was to develop a new simpler and more effective severity score for communityacquired pneumonia (CAP) patients. A total of 1640 consecutive hospitalized CAP patients in Second Affiliated Hospital of Zhejiang University were included. The effectiveness of different pneumonia severity scores to predict mortality was compared, and the performance of the new score was validated on an external cohort of 1164 patients with pneumonia admitted to a teaching hospital in Italy. Using age≥ 65 years, LDH>230u/L, albumin<3.5g/dL, platelet count<100×109/L, confusion, urea>7mmol/L, respiratory rate≥30/min, low blood pressure, we assembled a new severity score named as expanded-CURB-65. The 30-day mortality and length of stay were increased along with increased risk score. The AUCs in the prediction of 30-day mortality in the main cohort were 0.826 (95%CI, 0.807–0.844), 0.801 (95%CI, 0.781–0.820), 0.756 (95%CI, 0.735–0.777), 0.793 (95%CI, 0.773–0.813) and 0.759 (95%CI, 0.737–0.779) for the expanded-CURB-65, PSI, CURB-65, SMART-COP and A-DROP, respectively. The performance of this bedside score was confirmed in CAP patients of the validation cohort although calibration was not successful in patients with health care-associated pneumonia (HCAP). The expanded CURB-65 is objective, simpler and more accurate scoring system for evaluation of CAP severity, and the predictive efficiency was better than other score systems

Disturbance Rejection Control for Autonomous Trolley Collection Robots with Prescribed Performance

Trajectory tracking control of autonomous trolley collection robots (ATCR) is an ambitious work due to the complex environment, serious noise and external disturbances. This work investigates a control scheme for ATCR subjecting to severe environmental interference. A kinematics model based adaptive sliding mode disturbance observer with fast convergence is first proposed to estimate the lumped disturbances. On this basis, a robust controller with prescribed performance is proposed using a backstepping technique, which improves the transient performance and guarantees fast convergence. Simulation outcomes have been provided to illustrate the effectiveness of the proposed control scheme

DNA binding mechanism revealed by high resolution crystal structure of Arabidopsis thaliana WRKY1 protein

WRKY proteins, defined by the conserved WRKYGQK sequence, are comprised of a large superfamily of transcription factors identified specifically from the plant kingdom. This superfamily plays important roles in plant disease resistance, abiotic stress, senescence as well as in some developmental processes. In this study, the Arabidopsis WRKY1 was shown to be involved in the salicylic acid signaling pathway and partially dependent on NPR1; a C-terminal domain of WRKY1, AtWRKY1-C, was constructed for structural studies. Previous investigations showed that DNA binding of the WRKY proteins was localized at the WRKY domains and these domains may define novel zinc-binding motifs. The crystal structure of the AtWRKY1-C determined at 1.6 Å resolution has revealed that this domain is composed of a globular structure with five β strands, forming an antiparallel β-sheet. A novel zinc-binding site is situated at one end of the β-sheet, between strands β4 and β5. Based on this high-resolution crystal structure and site-directed mutagenesis, we have defined and confirmed that the DNA-binding residues of AtWRKY1-C are located at β2 and β3 strands. These results provided us with structural information to understand the mechanism of transcriptional control and signal transduction events of the WRKY proteins