The effect of β-cyclocitral treatment on the carotenoid content of transgenic Marsh grapefruit (Citrus paradisi Macf.) suspension-cultured cells

Zheng, Xiongjie, Zhu, Kaijie, Ye, Junli, Price, Elliott J., Deng, Xiuxin, Fraser, Paul D. (2020): The effect of β-cyclocitral treatment on the carotenoid content of transgenic Marsh grapefruit (Citrus paradisi Macf.) suspension-cultured cells. Phytochemistry (112509) 180: 1-8, DOI: 10.1016/j.phytochem.2020.112509, URL: http://dx.doi.org/10.1016/j.phytochem.2020.11250

EmotionPrompt: Leveraging Psychology for Large Language Models Enhancement via Emotional Stimulus

Large language models (LLMs) have achieved significant performance in many fields such as reasoning, language understanding, and math problem-solving, and are regarded as a crucial step to artificial general intelligence (AGI). However, the sensitivity of LLMs to prompts remains a major bottleneck for their daily adoption. In this paper, we take inspiration from psychology and propose EmotionPrompt to explore emotional intelligence to enhance the performance of LLMs. EmotionPrompt operates on a remarkably straightforward principle: the incorporation of emotional stimulus into prompts. Experimental results demonstrate that our EmotionPrompt, using the same single prompt templates, significantly outperforms original zero-shot prompt and Zero-shot-CoT on 8 tasks with diverse models: ChatGPT, Vicuna-13b, Bloom, and T5. Further, EmotionPrompt was observed to improve both truthfulness and informativeness. We believe that EmotionPrompt heralds a novel avenue for exploring interdisciplinary knowledge for humans-LLMs interaction.Comment: Work in progress; 9 page

CompeteAI: Understanding the Competition Behaviors in Large Language Model-based Agents

Large language models (LLMs) have been widely used as agents to complete different tasks, such as personal assistance or event planning. While most work has focused on cooperation and collaboration between agents, little work explores competition, another important mechanism that fosters the development of society and economy. In this paper, we seek to examine the competition behaviors in LLM-based agents. We first propose a general framework to study the competition between agents. Then, we implement a practical competitive environment using GPT-4 to simulate a virtual town with two types of agents, including restaurant agents and customer agents. Specifically, restaurant agents compete with each other to attract more customers, where the competition fosters them to transform, such as cultivating new operating strategies. The results of our experiments reveal several interesting findings ranging from social learning to Matthew Effect, which aligns well with existing sociological and economic theories. We believe that competition between agents deserves further investigation to help us understand society better. The code will be released soon.Comment: Technical report; 21 page

Clustering-Structure Representative Sampling from Graph Streams

Most existing sampling algorithms on graphs (i.e., network-structured data) focus on sampling from memory-resident static graphs and assume the entire graphs are always available. However, the graphs encountered in modern applications are often too large and/or too dynamic to be processed with limited memory.Furthermore, existing sampling techniques are inadequate for preserving the inherent clustering structure, which is an essential property of complex networks.To tackle these problems, we propose a new sampling algorithm that dynamically maintains a representative sample and is capable of retaining clustering structure in graph streams at any time.Performance of the proposed algorithm is evaluated through empirical experiments using real-world networks. The experimental results have shown that our proposed \textit{CPIES} algorithm can produce clustering-structure representative samples and outperforms current online sampling algorithms

PromptBench: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts

The increasing reliance on Large Language Models (LLMs) across academia and industry necessitates a comprehensive understanding of their robustness to prompts. In response to this vital need, we introduce PromptBench, a robustness benchmark designed to measure LLMs' resilience to adversarial prompts. This study uses a plethora of adversarial textual attacks targeting prompts across multiple levels: character, word, sentence, and semantic. These prompts are then employed in diverse tasks, such as sentiment analysis, natural language inference, reading comprehension, machine translation, and math problem-solving. Our study generates 4,032 adversarial prompts, meticulously evaluated over 8 tasks and 13 datasets, with 567,084 test samples in total. Our findings demonstrate that contemporary LLMs are vulnerable to adversarial prompts. Furthermore, we present comprehensive analysis to understand the mystery behind prompt robustness and its transferability. We then offer insightful robustness analysis and pragmatic recommendations for prompt composition, beneficial to both researchers and everyday users. We make our code, prompts, and methodologies to generate adversarial prompts publicly accessible, thereby enabling and encouraging collaborative exploration in this pivotal field: https://github.com/microsoft/promptbench.Comment: Technical report; 23 pages; code is at: https://github.com/microsoft/promptbenc

A Survey on Evaluation of Large Language Models

Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes increasingly critical, not only at the task level, but also at the society level for better understanding of their potential risks. Over the past years, significant efforts have been made to examine LLMs from various perspectives. This paper presents a comprehensive review of these evaluation methods for LLMs, focusing on three key dimensions: what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide an overview from the perspective of evaluation tasks, encompassing general natural language processing tasks, reasoning, medical usage, ethics, educations, natural and social sciences, agent applications, and other areas. Secondly, we answer the `where' and `how' questions by diving into the evaluation methods and benchmarks, which serve as crucial components in assessing performance of LLMs. Then, we summarize the success and failure cases of LLMs in different tasks. Finally, we shed light on several future challenges that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to researchers in the realm of LLMs evaluation, thereby aiding the development of more proficient LLMs. Our key point is that evaluation should be treated as an essential discipline to better assist the development of LLMs. We consistently maintain the related open-source materials at: https://github.com/MLGroupJLU/LLM-eval-survey.Comment: 23 page

CYP2C19 genotype and platelet aggregation test-guided dual antiplatelet therapy after off-pump coronary artery bypass grafting: A retrospective cohort study

BackgroundDual antiplatelet therapy (DAPT) is recommended in patients undergoing off-pump coronary artery bypass graft surgery (OPCAB). Clopidogrel is less effective among patients with loss-of-function (LoF) of CYP2C19 alleles, while ticagrelor has direct effects on P2Y12 receptor. Whether a CYP2C19 genotype plus platelet aggregation test (PAgT)-guided DAPT after CABG could improve clinical outcomes remain uncertain.Materials and methodsFrom August 2019 to December 2020, 1,134 consecutive patients who underwent OPCAB received DAPT for 1 year after surgery in Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. According to the actual treatment they received in real-world, 382 (33.7%) of them received a traditional DAPT: aspirin 100 mg qd + clopidogrel 75 mg qd, no matter the CYP2C19 genotype and response in platelet aggregation test (PAgT). The other 752 (66.3%) patients received an individual DAPT based on CYP2C19 genotype and PAgT: aspirin 100 mg qd + clopidogrel 75 mg qd if CYP2C19 was extensive metabolizer, or moderate metabolizer but normal response in PAgT; aspirin 100 mg qd + ticagrelor 90 mg bid if CYP2C19 was poor metabolizer, or moderate metabolizer but no or low response in PAgT. One-year follow-up was achieved for all patients. The primary outcome was major adverse cardiovascular events (MACE), a composite of cardiovascular death, myocardial infarction, and stroke. The safety outcome was thrombolysis in myocardial infarction (TIMI) criteria major bleeding.ResultsCompared with the traditional DAPT group, the risk of MACE in the individual DAPT group was significantly lower (5.5 vs. 9.2%, HR 0.583; 95% CI, 0.371–0.915; P = 0.019), mainly due to the decreased risk of MI (1.7 vs. 4.2%, HR 0.407; 95% CI, 0.196–0.846; P = 0.016). The risk of TIMI major bleeding events was similar between the two groups (5.3 vs. 6.0%, RR 0.883; 95% CI, 0.537–1.453; P = 0.626).ConclusionFor patients who underwent OPCAB, individual DAPT (CYP2C19 genotype plus PAgT-guided strategy) was associated with a lower risk of MACE and a similar risk of major bleeding

Cognitive impairment in diffuse axonal injury patients with favorable outcome

Background and purposeTraumatic brain injury (TBI), especially the severe TBI are often followed by persistent cognitive sequalae, including decision-making difficulties, reduced neural processing speed and memory deficits. Diffuse axonal injury (DAI) is classified as one of the severe types of TBI. Part of DAI patients are marginalized from social life due to cognitive impairment, even if they are rated as favorable outcome. The purpose of this study was to elucidate the specific type and severity of cognitive impairment in DAI patients with favorable outcome.MethodsThe neurocognition of 46 DAI patients with favorable outcome was evaluated by the Chinese version of the Montreal Cognitive Assessment Basic (MoCA-BC), and the differences in the domains of cognitive impairment caused by different grades of DAI were analyzed after data conversion of scores of nine cognitive domains of MoCA-BC by Pearson correlation analysis.ResultsAmong the 46 DAI patients with favorable outcome, eight had normal cognitive function (MoCA-BC ≥ 26), and 38 had cognitive impairment (MoCA-BC < 26). The MoCA-BC scores were positively correlated with pupillary light reflex (r = 0.361, p = 0.014), admission Glasgow Coma Scale (GCS) (r = 0.402, p = 0.006), and years of education (r = 0.581, p < 0.001). Return of consciousness (r = −0.753, p < 0.001), Marshall CT (r = −0.328, p = 0.026), age (r = −0.654, p < 0.001), and DAI grade (r = −0.403, p = 0.006) were found to be negatively correlated with the MoCA-BC scores. In patients with DAI grade 1, the actually deducted scores (Ads) of memory (r = 0.838, p < 0.001), abstraction (r = 0.843, p < 0.001), and calculation (r = 0.782, p < 0.001) were most related to the Ads of MoCA-BC. The Ads of nine cognitive domains and MoCA-BC were all proved to be correlated, among patients with DAI grade 2. However, In the DAI grade 3 patients, the highest correlation with the Ads of MoCA-BC were the Ads of memory (r = 0.904, p < 0.001), calculation (r = 0.799, p = 0.006), orientation (r = 0.801, p = 0.005), and executive function (r = 0.869, p = 0.001).ConclusionDAI patients with favorable outcome may still be plagued by cognitive impairment, and different grades of DAI cause different domains of cognitive impairment