Evaluation and Analysis of Hallucination in Large Vision-Language Models

Large Vision-Language Models (LVLMs) have recently achieved remarkable success. However, LVLMs are still plagued by the hallucination problem, which limits the practicality in many scenarios. Hallucination refers to the information of LVLMs' responses that does not exist in the visual input, which poses potential risks of substantial consequences. There has been limited work studying hallucination evaluation in LVLMs. In this paper, we propose Hallucination Evaluation based on Large Language Models (HaELM), an LLM-based hallucination evaluation framework. HaELM achieves an approximate 95% performance comparable to ChatGPT and has additional advantages including low cost, reproducibility, privacy preservation and local deployment. Leveraging the HaELM, we evaluate the hallucination in current LVLMs. Furthermore, we analyze the factors contributing to hallucination in LVLMs and offer helpful suggestions to mitigate the hallucination problem. Our training data and human annotation hallucination data will be made public soon.Comment: 11 pages, 5 figure

mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality

Large language models (LLMs) have demonstrated impressive zero-shot abilities on a variety of open-ended tasks, while recent research has also explored the use of LLMs for multi-modal generation. In this study, we introduce mPLUG-Owl, a novel training paradigm that equips LLMs with multi-modal abilities through modularized learning of foundation LLM, a visual knowledge module, and a visual abstractor module. This approach can support multiple modalities and facilitate diverse unimodal and multimodal abilities through modality collaboration. The training paradigm of mPLUG-Owl involves a two-stage method for aligning image and text, which learns visual knowledge with the assistance of LLM while maintaining and even improving the generation abilities of LLM. In the first stage, the visual knowledge module and abstractor module are trained with a frozen LLM module to align the image and text. In the second stage, language-only and multi-modal supervised datasets are used to jointly fine-tune a low-rank adaption (LoRA) module on LLM and the abstractor module by freezing the visual knowledge module. We carefully build a visually-related instruction evaluation set OwlEval. Experimental results show that our model outperforms existing multi-modal models, demonstrating mPLUG-Owl's impressive instruction and visual understanding ability, multi-turn conversation ability, and knowledge reasoning ability. Besides, we observe some unexpected and exciting abilities such as multi-image correlation and scene text understanding, which makes it possible to leverage it for harder real scenarios, such as vision-only document comprehension. Our code, pre-trained model, instruction-tuned models, and evaluation set are available at https://github.com/X-PLUG/mPLUG-Owl. The online demo is available at https://www.modelscope.cn/studios/damo/mPLUG-Owl.Comment: Working in Proces

Advances in the research, diagnosis and treatment of renal cell carcinoma in 2022

Renal cell carcinoma (RCC) is one of the three major urinary system tumors. With the changes of lifestyle and the rise of obesity, hypertension and other diseases, the incidence of RCC is increasing. The onset of RCC is hidden, and RCC has strong heterogeneity. Most RCC patients are found accidentally by imaging examination, so many patients were diagnosed in the advanced stage. Although the emergence of targeted therapy and immunotherapy has greatly prolonged the survival time of patients with advanced RCC, due to many pathological types of RCC, it is still difficult for many patients to benefit from the systematic treatment. Many basic and clinical studies are devoted to the development of new targets or drugs to prolong the survival time of patients. This article reviewed the advances in the research, diagnosis and treatment of RCC in 2022

Finite-Time Consensus Algorithm for Multiple Nonholonomic Disturbed Systems with Its Application

This paper deals with the problem of finite-time consensus of multiple nonholonomic disturbed systems. To accomplish this problem, the multiple nonholonomic systems are transformed into two multiple subsystems, and these two multiple subsystems are studied, respectively. For these two multiple subsystems, the terminal sliding mode (TSM) algorithms are designed, respectively, which achieve the finite-time reaching of sliding surface. Next, a switching control strategy is proposed to guarantee the finite-time consensus of all the states for multiple nonholonomic systems with disturbances. Finally, we demonstrate the effectiveness of the proposed consensus algorithms with application to multiple nonholonomic mobile robots

Prognostic significance of the dynamic changes of systemic inflammatory response in metastatic renal cell carcinoma

ABSTRACT Purpose: To elucidate the prognostic value of systemic inflammatory response in patients with metastatic renal cell carcinoma (mRCC) who are treated with sunitinib, we evaluated the prognostic role of C-reactive protein (CRP) kinetics. This study also compared prognostic models containing CRP kinetics and neutrophil-to-lymphocyte ratio (NLR) kinetics. Materials and Methods: A consecutive cohort of 94 patients with mRCC who were treated with sunitinib was retrospectively included from Fudan University Shanghai Cancer Center. According to dynamic changes in CRP and the NLR, patients were divided into three groups for analysis of CRP and NLR kinetics. The associations between survival and potential prognostic factors were assessed. The incremental value of prognostication was evaluated. Results: A significant difference (P<0.001) in overall survival (OS) was observed among the three groups of CRP kinetics. The median OS of the non-elevated group was nearly 1.3-fold longer than that of the normalized group (33.0 vs. 26.3 months), and two times longer than that of the non-normalized group (33.0 vs. 14.0 months). Multivariate analysis showed that CRP and NLR kinetics were independent prognostic indicators. The model containing CRP kinetics had a better predictive accuracy than that with NLR kinetics, which was supported by the C-index (0.731 vs. 0.684) and the likelihood ratio χ2 test (79.9% vs. 44.9%). Conclusion: Our study suggests that dynamic changes in CRP can better predict survival in patients with mRCC who are treated with sunitinib. Routine assessment of CRP before and after targeted therapy would help identify patients at risk of a poor outcome

A 5-lncRNA Signature Associated with Smoking Predicts the Overall Survival of Patients with Muscle-Invasive Bladder Cancer

Increasing evidence demonstrated that noncoding RNA is abnormally expressed in cancer tissues and serves a vital role in tumorigenesis, tumor development, and metastasis. The aim of the present study was to determine an lncRNA signature in order to predict the overall survival (OS) of patients with muscle-invasive bladder cancer (MIBC). A total of 246 patients with pathologically confirmed MIBC in The Cancer Genome Atlas (TCGA) dataset were recruited and included in the present study. We choose patients who have smoked less (including never smoking) or more than 15 years. A total of 44 differentially expressed lncRNAs were identified with a fold change larger than 1.5 and a P value 15 years was performed by using the matchIt package. Among the 44 differentially expressed lncRNAs, 5 lncRNAs were identified to be significantly associated with OS. Based on the characteristic risk scores of these 5 lncRNAs, patients were divided into low-risk and high-risk groups and exhibited significant differences in OS. Multivariate Cox regression analysis demonstrated that the 5-lncRNA signature was independent of age, tumor-node metastasis (TNM) staging, lymphatic node status, and adjuvant postoperative radiotherapy. In the present study, a novel 5-lncRNA signature was developed and was demonstrated to be useful in predicting the survival of patients with MIBC. If validated, this lncRNA signature may assist in the selection of a high-risk subpopulation that requires more aggressive therapeutic intervention. The risk scores involved in several associated pathways were identified using gene set enrichment analysis (GSEA). However, the clinical implications and mechanism of these 5 lncRNAs require further investigation

Construction of an Immune Escape-Related Signature in Clear Cell Renal Cell Carcinoma and Identification of the Relationship between IFNAR1 and Immune Infiltration by Multiple Immunohistochemistry

Background: In the past decade, immunotherapy has been widely used in the treatment of various tumors, such as PD-1/PD-L1 inhibitors. Although clear cell renal cell carcinoma (ccRCC) has been shown to be sensitive to immunotherapy, it is effective only in several cases, which brings great obstacles to anti-tumor therapy for patients. Lawson et al. have successfully identified 182 “core cancer innate immune escape genes” whose deletion makes cancer cells more sensitive or resistant to T-cell attack. Methods: In this research, we sought to explore genes closely associated with ccRCC among the 182 core cancer innate immune escape genes. We used online databases to screen mutated genes in ccRCC, and then used ConsensusClusterPlus to cluster clinical samples to analyze differences in clinical prognosis and immune components between the two subgroups. In addition, the immune escape score was calculated using lasso cox regression, and a stable tumor immune escape-related nomogram was established to predict the overall survival of patients. Results: Higher immune escape score was significantly correlated with shorter survival time. Meanwhile, through the validation of the external cohort and the correlation analysis of the immune microenvironment, we proved that IFNAR1 is the key gene regulating immune escape in ccRCC, and we also found that the function of IFNAR1 in promoting immune activation is achieved by facilitating the infiltration of CD4+ T cells and CD8+ T cells. IFNAR1 regulates the malignant behavior of ccRCC by inhibiting the proliferation and migration properties. Conclusions: IFNAR1 may become a key biomarker for evaluating the efficacy of ccRCC immunotherapy and may also be a potential target for immunotherapy

Prognostic value of components of body composition in patients treated with targeted therapy for advanced renal cell carcinoma: a retrospective case series.

BACKGROUND:To evaluate the association between various components of body composition and overall survival of patients treated with targeted therapies for advanced renal cell carcinoma. METHODS:This retrospective study included 124 Chinese patients with advanced renal cell carcinoma who had been treated with targeted therapy from 2008 to 2012 at Fudan University Cancer Center. The L3 plane from a computed tomography scan was analyzed. Area and density were recorded as quantitative and quality measures. Univariate and multivariate Cox proportion hazard regression models were constructed to calculate the crude and adjusted hazard ratio (HR) of various components of body composition for overall survival. X-tile software was used to search for optimal cutoffs for male and female patients and the concordance index evaluated incremental changes in prognostication. RESULTS:After adjusting for age, sex and Heng risk stratification, only visceral adipose tissue index (HR 0.981, p = 0.002) and subcutaneous adipose tissue index (HR 0.987, p = 0.048) were independently associated with overall survival. Visceral adipose tissue remained a significant prognostic factor (HR 0.997, p = 0.005) when the influence of body mass index was included. Using defined cutoffs, patients with low VAT had double the death rate (p = 0.007). Visceral adipose tissue also added significant benefit to Heng risk stratification. Further exploratory analysis revealed that visceral adipose tissue may be an indicator of nutritional status in patients with advanced renal cell carcinoma. CONCLUSION:Radiologic measurement of VAT is an independent prognostic factor for Asian patients treated with targeted therapy for advanced renal cell carcinoma