Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language Model

Integrating large language models (LLMs) into healthcare presents potential but faces challenges. Directly pre-training LLMs for domains like medicine is resource-heavy and sometimes unfeasible. Sole reliance on Supervised Fine-tuning (SFT) can result in overconfident predictions and may not tap into domain specific insights. Addressing these challenges, we present a multi-stage training method combining Domain-specific Continued Pre-training (DCPT), SFT, and Direct Preference Optimization (DPO). A notable contribution of our study is the introduction of a 3Gb Chinese Medicine (ChiMed) dataset, encompassing medical question answering, plain texts, knowledge graphs, and dialogues, segmented into three training stages. The medical LLM trained with our pipeline, Qilin-Med, exhibits significant performance boosts. In the CPT and SFT phases, it achieves 38.4% and 40.0% accuracy on the CMExam, surpassing Baichuan-7B's 33.5%. In the DPO phase, on the Huatuo-26M test set, it scores 16.66 in BLEU-1 and 27.44 in ROUGE1, outperforming the SFT's 12.69 and 24.21. This highlights the strength of our training approach in refining LLMs for medical applications

Is ChatGPT a Good Recommender? A Preliminary Study

Recommendation systems have witnessed significant advancements and have been widely used over the past decades. However, most traditional recommendation methods are task-specific and therefore lack efficient generalization ability. Recently, the emergence of ChatGPT has significantly advanced NLP tasks by enhancing the capabilities of conversational models. Nonetheless, the application of ChatGPT in the recommendation domain has not been thoroughly investigated. In this paper, we employ ChatGPT as a general-purpose recommendation model to explore its potential for transferring extensive linguistic and world knowledge acquired from large-scale corpora to recommendation scenarios. Specifically, we design a set of prompts and evaluate ChatGPT's performance on five recommendation scenarios. Unlike traditional recommendation methods, we do not fine-tune ChatGPT during the entire evaluation process, relying only on the prompts themselves to convert recommendation tasks into natural language tasks. Further, we explore the use of few-shot prompting to inject interaction information that contains user potential interest to help ChatGPT better understand user needs and interests. Comprehensive experimental results on Amazon Beauty dataset show that ChatGPT has achieved promising results in certain tasks and is capable of reaching the baseline level in others. We conduct human evaluations on two explainability-oriented tasks to more accurately evaluate the quality of contents generated by different models. And the human evaluations show ChatGPT can truly understand the provided information and generate clearer and more reasonable results. We hope that our study can inspire researchers to further explore the potential of language models like ChatGPT to improve recommendation performance and contribute to the advancement of the recommendation systems field.Comment: Accepted by CIKM 2023 GenRec Worksho

Mapping World Scientific Collaboration on the Research of COVID-19: Authors, Journals, Institutions, and Countries

The COVID-19 (2019 novel Coronavirus) is the most widespread pandemic infectious disease encountered in human history. Its economic losses and the number of countries involved rank first in the history of human viruses. After the outbreak, researchers in the field of medicine quickly carried out scientific research on the virus. Through a visual analysis of relevant scientific research papers from January 1st to April 1st, 2020, we can grasp the worldwide scientific research cooperation situation of 2019-nCoV research and reflect the international collaboration in combating the pandemic. To this end, 415 papers indexed in Thomson Reuters’s Web of Science were studied to provide a visualized description of scientific collaborations across the world by multiple levels, including author level, journal level, institution level and country level

Clue-based Spatio-textual Query

Along with the proliferation of online digital map and location-based service, very large POI (point of interest) databases have been constructed where a record corresponds to a POI with information including name, category, address, geographical location and other features. A basic spatial query in POI database is POI retrieval. In many scenarios, a user cannot provide enough information to pinpoint the POI except some clue. For example, a user wants to identify a caf é in a city visited many years ago. SHe cannot remember the name and address but she still recalls that "the caf é is about 200 meters away from a restaurant; and turning left at the restaurant there is a bakery 500 meters away, etc.". Intuitively, the clue, even partial and approximate, describes the spatio-textual context around the targeted POI. Motivated by this observation, this work investigates clue-based spatio-textual query which allows user providing clue, i.e., some nearby POIs and the spatial relationships between them, in POI retrieval. The objective is to retrieve k POIs from a POI database with the highest spatio-textual context similarities against the clue. This work has deliberately designed data-quality-tolerant spatio-textual context similarity metric to cope with various data quality problems in both the clue and the POI database. Through crossing valuation, the query accuracy is further enhanced by ensemble method. Also, this work has developed an index called roll-out-star R-tree (RSR-tree) to dramatically improve the query processing efficiency. The extensive tests on data sets from the real world have verified the superiority of our methods in all aspects. </jats:p

Oligorotaxane radicals under orders

A strategy for creating foldameric oligorotaxanes composed of only positively charged components is reported. Threadlike components-namely oligoviologens-in which different numbers of 4,4'-bipyridinium (BIPY(2+)) subunits are linked by p-xylylene bridges, are shown to be capable of being threaded by cyclobis(paraquat-p-phenylene) (CBPQT(4+)) rings following the introduction of radical-pairing interactions under reducing conditions. UV/vis/NIR spectroscopic and electrochemical investigations suggest that the reduced oligopseudorotaxanes fold into highly ordered secondary structures as a result of the formation of BIPY(\u2022+) radical cation pairs. Furthermore, by installing bulky stoppers at each end of the oligopseudorotaxanes by means of Cu-free alkyne-azide cycloadditions, their analogous oligorotaxanes, which retain the same stoichiometries as their progenitors, can be prepared. Solution-state studies of the oligorotaxanes indicate that their mechanically interlocked structures lead to the enforced interactions between the dumbbell and ring components, allowing them to fold (contract) in their reduced states and unfold (expand) in their fully oxidized states as a result of Coulombic repulsions. This electrochemically controlled reversible folding and unfolding process, during which the oligorotaxanes experience length contractions and expansions, is reminiscent of the mechanisms of actuation associated with muscle fibers

Pretreatment with antiplatelet drugs improves the cardiac function after myocardial infarction without reperfusion in a mouse model

Background: Reperfusion therapy is known to improve prognosis and limit myocardial damage aftermyocardial infarction (MI). The administration of antiplatelet drugs prior to percutaneous coronaryintervention also proves beneficial to patients with acute MI (AMI). However, a good number of AMIpatients do not receive reperfusion therapy, and it is not clear if they would benefit from antiplateletpre-treatment.Methods: Experimental C57BL/6 mice were randomly allocated to five groups: the sham group,control, post-treatment, pre-treatment, and pre- and post-treatment groups. Acetylsalicylic acid (15 mg/kg), clopidogrel (11 mg/kg), ticagrelor (27 mg/kg), and prasugrel (1.5 mg/kg) were intragastrically administered in the treatment groups. On day 7 post MI, cardiac function and cardiac fibrosis were evaluated using echocardiography and Masson’s trichrome staining, respectively. Histopathological examinations were performed on tissue sections to grade inflammatory cell infiltration. Platelet inhibition was monitored by measuring thrombin-induced platelet aggregation.Results: Left ventricular ejection fraction and fractional shortening improved significantly (p &lt; 0.01)in the pre-treatment groups when compared to the post-treatment and control groups. A significant(p &lt; 0.01) decrease in cardiac fibrosis was observed in the pre-treatment group, compared with the posttreatment and control groups. Inflammatory cell infiltration significantly decreased in the pre-treatment group compared with the control group (p &lt; 0.05). Thrombin-induced platelet aggregation was significantly inhibited by antiplatelet drugs, but increased with the exposure to H2O2.Conclusions: In the absence of reperfusion therapy, pre-treatment with antiplatelet drugs successfullyimproved cardiac function, reduced cardiac fibrosis and inflammatory cell infiltration, and inhibited oxidative stress-induced platelet aggregation after MI in the mouse model