Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification

Recent progress in large language models (LLMs) like GPT-4 and PaLM-2 has brought significant advancements in addressing math reasoning problems. In particular, OpenAI's latest version of GPT-4, known as GPT-4 Code Interpreter, shows remarkable performance on challenging math datasets. In this paper, we explore the effect of code on enhancing LLMs' reasoning capability by introducing different constraints on the \textit{Code Usage Frequency} of GPT-4 Code Interpreter. We found that its success can be largely attributed to its powerful skills in generating and executing code, evaluating the output of code execution, and rectifying its solution when receiving unreasonable outputs. Based on this insight, we propose a novel and effective prompting method, explicit \uline{c}ode-based \uline{s}elf-\uline{v}erification~(CSV), to further boost the mathematical reasoning potential of GPT-4 Code Interpreter. This method employs a zero-shot prompt on GPT-4 Code Interpreter to encourage it to use code to self-verify its answers. In instances where the verification state registers as ``False'', the model shall automatically amend its solution, analogous to our approach of rectifying errors during a mathematics examination. Furthermore, we recognize that the states of the verification result indicate the confidence of a solution, which can improve the effectiveness of majority voting. With GPT-4 Code Interpreter and CSV, we achieve an impressive zero-shot accuracy on MATH dataset \textbf{(53.9\% $\to$ 84.3\%)}.Comment: Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verificatio

TeacherLM: Teaching to Fish Rather Than Giving the Fish, Language Modeling Likewise

Large Language Models (LLMs) exhibit impressive reasoning and data augmentation capabilities in various NLP tasks. However, what about small models? In this work, we propose TeacherLM-7.1B, capable of annotating relevant fundamentals, chain of thought, and common mistakes for most NLP samples, which makes annotation more than just an answer, thus allowing other models to learn "why" instead of just "what". The TeacherLM-7.1B model achieved a zero-shot score of 52.3 on MMLU, surpassing most models with over 100B parameters. Even more remarkable is its data augmentation ability. Based on TeacherLM-7.1B, we augmented 58 NLP datasets and taught various student models with different parameters from OPT and BLOOM series in a multi-task setting. The experimental results indicate that the data augmentation provided by TeacherLM has brought significant benefits. We will release the TeacherLM series of models and augmented datasets as open-source.Comment: 5 figures, 15 page

Visual analysis of lung neuroendocrine tumors based on CiteSpace knowledge graph

ObjectiveThe relevant literatures in the field of pulmonary neuroendocrine tumor were analyzed to understand the lineage, hot spots and development trends of research in this tumor.MethodThe Web of Science core collection was searched for English-language literature about neuroendocrine tumors of the lung published between 2000 and 2022. CiteSpace software was imported for visualization analysis of countries, institutions, co-cited authors and co-cited journals and sorting of high-frequency keywords, as well as co-cited references and keyword co-occurrence, clustering and bursting display.ResultsA total of 594 publications on neuroendocrine tumours of the lung were available, from 2000 to 2022, with an overall upward trend of annual publications in the literature. Authors or institutions from the United States, Italy, Japan and China were more active in this field, but there was little cooperation among the major countries. Co-cited references and keyword co-occurrence and cluster analysis showed that research on diagnostic instruments, pathogenesis, ectopic ACTH signs, staging and prognosis and treatment was a current research hotspot. The keyword bursts suggested that therapeutic approaches might be a key focus of future research into the field for pulmonary neuroendocrine tumors.ConclusionOver these 20 years, research related to neuroendocrine tumors of the lung has increased in fervour, with research on diagnostic instruments, pathogenesis, ectopic ACTH signs, staging and prognosis, and treatment being the main focus of research. Therapeutic treatments may be the future research trend in this field

Observation of Rydberg moir\'e excitons

Rydberg excitons, the solid-state counterparts of Rydberg atoms, have sparked considerable interest in harnessing their quantum application potentials, whereas a major challenge is realizing their spatial confinement and manipulation. Lately, the rise of two-dimensional moir\'e superlattices with highly tunable periodic potentials provides a possible pathway. Here, we experimentally demonstrate this capability through the observation of Rydberg moir\'e excitons (XRM), which are moir\'e trapped Rydberg excitons in monolayer semiconductor WSe2 adjacent to twisted bilayer graphene. In the strong coupling regime, the XRM manifest as multiple energy splittings, pronounced redshift, and narrowed linewidth in the reflectance spectra, highlighting their charge-transfer character where electron-hole separation is enforced by the strongly asymmetric interlayer Coulomb interactions. Our findings pave the way for pursuing novel physics and quantum technology exploitation based on the excitonic Rydberg states.Comment: 24 pages, including 4 figures and 6 supplementary figure

Lithium in Cancer Therapy: Friend or Foe?

Lithium, a trace element important for fetal health and development, is considered a metal drug with a well-established clinical regime, economical production process, and a mature storage system. Several studies have shown that lithium affects tumor development by regulating inositol monophosphate (IMPase) and glycogen synthase kinase-3 (GSK-3). Lithium can also promote proliferation and programmed cell death (PCD) in tumor cells through a number of new targets, such as the nuclear receptor NR4A1 and Hedgehog-Gli. Lithium may increase cancer treatment efficacy while reducing side effects, suggesting that it can be used as an adjunctive therapy. In this review, we summarize the effects of lithium on tumor progression and discuss the underlying mechanisms. Additionally, we discuss lithium’s limitations in antitumor clinical applications, including its narrow therapeutic window and potential pro-cancer effects on the tumor immune system

Lithium in Cancer Therapy: Friend or Foe?

Lithium, a trace element important for fetal health and development, is considered a metal drug with a well-established clinical regime, economical production process, and a mature storage system. Several studies have shown that lithium affects tumor development by regulating inositol monophosphate (IMPase) and glycogen synthase kinase-3 (GSK-3). Lithium can also promote proliferation and programmed cell death (PCD) in tumor cells through a number of new targets, such as the nuclear receptor NR4A1 and Hedgehog-Gli. Lithium may increase cancer treatment efficacy while reducing side effects, suggesting that it can be used as an adjunctive therapy. In this review, we summarize the effects of lithium on tumor progression and discuss the underlying mechanisms. Additionally, we discuss lithium’s limitations in antitumor clinical applications, including its narrow therapeutic window and potential pro-cancer effects on the tumor immune system

Subsidence Monitoring and Mechanism Analysis of Anju Airport in Suining Based on InSAR and Numerical Simulation

The mountainous area of southwest China is characterized by significant topography and complex geological conditions, which pose great challenges to the airport’s site selection, construction, and safe operation. Suining Anju Airport, one of the key projects under construction in southwest China, is essential in alleviating and dredging the air passenger flow in Sichuan Province. Because the overlying quaternary strata’s physical and mechanical properties, thickness, and distribution range are fairly different in the longitudinal and transverse directions, the Anju Airport’s foundation in the hilly area has typical inhomogeneity. Large-scale excavation and filling pose a challenge to the ground stability of the airport. To comprehensively monitor Anju Airport’s uneven ground subsidence during the construction period, this paper selected SAR image data collected by the Sentinel-1A satellite from May 2018 to June 2021 to extract time-series ground subsidence measurements based on the SBAS-InSAR method. Furthermore, based on the simulation of roadbed filling in the airport’s parallel slide fill area, the dynamic evolution analysis of soil stress field and internal subsidence caused by roadbed filling activities was carried out to further reveal the occurrence mechanism of ground subsidence. The monitoring results show that the subsidence centers of Anju Airport are mainly distributed in the filling areas, and the average annual subsidence is −20~−75 mm/yr from May 2018 to June 2021. Comparative analysis with in situ data indicates that the RMSE of InSAR monitoring results was ±6.12 mm. The numerical simulation shows that the subsidence of the airport parallel slide is mainly caused by a load of subgrade filling body and the compression of its weight. The results of this study can provide reference methodology and data support for the construction and future safe operation of Suining Anju Airport

VCSUM: A Versatile Chinese Meeting Summarization Dataset

Compared to news and chat summarization, the development of meeting summarization is hugely decelerated by the limited data. To this end, we introduce a versatile Chinese meeting summarization dataset, dubbed VCSum, consisting of 239 real-life meetings, with a total duration of over 230 hours. We claim our dataset is versatile because we provide the annotations of topic segmentation, headlines, segmentation summaries, overall meeting summaries, and salient sentences for each meeting transcript. As such, the dataset can adapt to various summarization tasks or methods, including segmentation-based summarization, multi-granularity summarization and retrieval-then-generate summarization. Our analysis confirms the effectiveness and robustness of VCSum. We also provide a set of benchmark models regarding different downstream summarization tasks on VCSum to facilitate further research. The dataset and code will be released at https://github.com/hahahawu/VCSum.Comment: Findings of ACL 2023 (long paper). GitHub: https://github.com/hahahawu/VCSu