KGQuiz: Evaluating the Generalization of Encoded Knowledge in Large Language Models

Large language models (LLMs) demonstrate remarkable performance on knowledge-intensive tasks, suggesting that real-world knowledge is encoded in their model parameters. However, besides explorations on a few probing tasks in limited knowledge domains, it is not well understood how to evaluate LLMs' knowledge systematically and how well their knowledge abilities generalize, across a spectrum of knowledge domains and progressively complex task formats. To this end, we propose KGQuiz, a knowledge-intensive benchmark to comprehensively investigate the knowledge generalization abilities of LLMs. KGQuiz is a scalable framework constructed from triplet-based knowledge, which covers three knowledge domains and consists of five tasks with increasing complexity: true-or-false, multiple-choice QA, blank filling, factual editing, and open-ended knowledge generation. To gain a better understanding of LLMs' knowledge abilities and their generalization, we evaluate 10 open-source and black-box LLMs on the KGQuiz benchmark across the five knowledge-intensive tasks and knowledge domains. Extensive experiments demonstrate that LLMs achieve impressive performance in straightforward knowledge QA tasks, while settings and contexts requiring more complex reasoning or employing domain-specific facts still present significant challenges. We envision KGQuiz as a testbed to analyze such nuanced variations in performance across domains and task formats, and ultimately to understand, evaluate, and improve LLMs' knowledge abilities across a wide spectrum of knowledge domains and tasks

Detecting Spoilers in Movie Reviews with External Movie Knowledge and User Networks

Online movie review platforms are providing crowdsourced feedback for the film industry and the general public, while spoiler reviews greatly compromise user experience. Although preliminary research efforts were made to automatically identify spoilers, they merely focus on the review content itself, while robust spoiler detection requires putting the review into the context of facts and knowledge regarding movies, user behavior on film review platforms, and more. In light of these challenges, we first curate a large-scale network-based spoiler detection dataset LCS and a comprehensive and up-to-date movie knowledge base UKM. We then propose MVSD, a novel Multi-View Spoiler Detection framework that takes into account the external knowledge about movies and user activities on movie review platforms. Specifically, MVSD constructs three interconnecting heterogeneous information networks to model diverse data sources and their multi-view attributes, while we design and employ a novel heterogeneous graph neural network architecture for spoiler detection as node-level classification. Extensive experiments demonstrate that MVSD advances the state-of-the-art on two spoiler detection datasets, while the introduction of external knowledge and user interactions help ground robust spoiler detection. Our data and code are available at https://github.com/Arthur-Heng/Spoiler-DetectionComment: EMNLP 202

Identification of genes regulated by Wnt/β-catenin pathway and involved in apoptosis via microarray analysis

BACKGROUND: Wnt/β-catenin pathway has critical roles in development and oncogenesis. Although significant progress has been made in understanding the downstream signaling cascade of this pathway, little is known regarding Wnt/β-catenin pathway modification of the cellular apoptosis. METHODS: To identify potential genes regulated by Wnt/β-catenin pathway and involved in apoptosis, we used a stably integrated, inducible RNA interference (RNAi) vector to specific inhibit the expression and the transcriptional activity of β-catenin in HeLa cells. Meanwhile, we designed an oligonucleotide microarray covering 1384 apoptosis-related genes. Using oligonucleotide microarrays, a series of differential expression of genes was identified and further confirmed by RT-PCR. RESULTS: Stably integrated inducible RNAi vector could effectively suppress β-catenin expression and the transcriptional activity of β-catenin/TCF. Meanwhile, depletion of β-catenin in this manner made the cells more sensitive to apoptosis. 130 genes involved in some important cell-apoptotic pathways, such as PTEN-PI3K-AKT pathway, NF-κB pathway and p53 pathway, showed significant alteration in their expression level after the knockdown of β-catenin. CONCLUSION: Coupling RNAi knockdown with microarray and RT-PCR analyses proves to be a versatile strategy for identifying genes regulated by Wnt/β-catenin pathway and for a better understanding the role of this pathway in apoptosis. Some of the identified β-catenin/TCF directed or indirected target genes may represent excellent targets to limit tumor growth

Solar Ring Mission: Building a Panorama of the Sun and Inner-heliosphere

Solar Ring (SOR) is a proposed space science mission to monitor and study the Sun and inner heliosphere from a full 360{\deg} perspective in the ecliptic plane. It will deploy three 120{\deg}-separated spacecraft on the 1-AU orbit. The first spacecraft, S1, locates 30{\deg} upstream of the Earth, the second, S2, 90{\deg} downstream, and the third, S3, completes the configuration. This design with necessary science instruments, e.g., the Doppler-velocity and vector magnetic field imager, wide-angle coronagraph, and in-situ instruments, will allow us to establish many unprecedented capabilities: (1) provide simultaneous Doppler-velocity observations of the whole solar surface to understand the deep interior, (2) provide vector magnetograms of the whole photosphere - the inner boundary of the solar atmosphere and heliosphere, (3) provide the information of the whole lifetime evolution of solar featured structures, and (4) provide the whole view of solar transients and space weather in the inner heliosphere. With these capabilities, Solar Ring mission aims to address outstanding questions about the origin of solar cycle, the origin of solar eruptions and the origin of extreme space weather events. The successful accomplishment of the mission will construct a panorama of the Sun and inner-heliosphere, and therefore advance our understanding of the star and the space environment that holds our life.Comment: 41 pages, 6 figures, 1 table, to be published in Advances in Space Researc

Well-posedness and asynchronous exponential growth of solutions of a two-phase cell division model

In this article we study a two-phase cell division model. The cells of the two different phases have different growth rates. We mainly consider the model of equal mitosis. By using the semigroup theory, we prove that this model is well-posed in suitable function spaces and its solutions have the property of asynchronous exponential growth as time approaches infinity. The corresponding model of asymmetric mitosis is also studied and similar results are obtained

How Do Morphological Factors Influence the Green Nut Yield of Chinese Torreya?

As an important economic tree species, Chinese Torreya (Torreya grandis cv Merrillii) has been widely planted in the subtropical regions of China. However, it remains to be studied whether morphological traits are the key factors reflecting or affecting the green nut yield of Chinese Torreya, which is necessary for breeding research and plantation management. Therefore, in Zhuji in the Zhejiang Province, the central production area of Chinese Torreya, we investigated the morphological traits (height, ground diameter, under-crown height, crown width, and branching amount) and green nut yield of 120 randomly selected Chinese Torreya. Our results indicated that the differences in the morphological traits among Chinese Torreya individuals were relatively small, but those in the green nut yield traits were great. There was highly significant (p r = 0.38; p r = 0.294; p p < 0.01). These findings imply that if the tree height is fixed, increasing the ground diameter and crown area, appropriately increasing the branching amount, and reducing the under-crown height could be potential technical measures to improve the green nut yield of Chinese Torreya. Our study provides background information on green nut yield and its morphological traits in Chinese Torreya

FactKB: Generalizable Factuality Evaluation using Language Models Enhanced with Factual Knowledge

Evaluating the factual consistency of automatically generated summaries is essential for the progress and adoption of reliable summarization systems. Despite recent advances, existing factuality evaluation models are not robust, being especially prone to entity and relation errors in new domains. We propose FactKB, a simple new approach to factuality evaluation that is generalizable across domains, in particular with respect to entities and relations. FactKB is based on language models pretrained using facts extracted from external knowledge bases. We introduce three types of complementary factuality pretraining objectives based on direct entity facts, facts grounded in auxiliary knowledge about entities, and facts constructed compositionally through knowledge base walks. The resulting factuality evaluation model achieves state-of-the-art performance on two in-domain news summarization benchmarks as well as on three out-of-domain scientific literature datasets. Further analysis of FactKB shows improved ability to detect erroneous entities and relations in summaries and is robust and generalizable across domains