KCTS: Knowledge-Constrained Tree Search Decoding with Token-Level Hallucination Detection

Large Language Models (LLMs) have demonstrated remarkable human-level natural language generation capabilities. However, their potential to generate misinformation, often called the hallucination problem, poses a significant risk to their deployment. A common approach to address this issue is to retrieve relevant knowledge and fine-tune the LLM with the knowledge in its input. Unfortunately, this method incurs high training costs and may cause catastrophic forgetting for multi-tasking models. To overcome these limitations, we propose a knowledge-constrained decoding method called KCTS (Knowledge-Constrained Tree Search), which guides a frozen LM to generate text aligned with the reference knowledge at each decoding step using a knowledge classifier score and MCTS (Monte-Carlo Tree Search). To adapt the sequence-level knowledge classifier to token-level guidance, we also propose a novel token-level hallucination detection method called RIPA (Reward Inflection Point Approximation). Our empirical results on knowledge-grounded dialogue and abstractive summarization demonstrate the strength of KCTS as a plug-and-play, model-agnostic decoding method that can effectively reduce hallucinations in natural language generation.Comment: Accepted at EMNLP 2023 Main Conferenc

Getting Sick After Seeing a Doctor? Diagnosing and Mitigating Knowledge Conflicts in Event Temporal Reasoning

Event temporal reasoning aims at identifying the temporal relations between two or more events. However, knowledge conflicts arise when there is a mismatch between the actual temporal relations of events in the context and the prior knowledge or biases learned by the model. We first systematically define distinct kinds of bias in event temporal reasoning, which include event relation prior bias, tense bias, narrative bias, and dependency bias, as indicators to study knowledge conflicts. To mitigate such event-related knowledge conflict, we introduce a Counterfactual Data Augmentation based method that can be applied to both Pre-trained Language Models (PLMs) and Large Language Models (LLMs) either as additional training data or demonstrations for In-Context Learning. Experiments suggest the importance of mitigating knowledge conflicts in event temporal reasoning tasks for reducing hallucination and highlight the potential of counterfactual data augmentation for improving model performance.Comment: 13 pages, 1 figur

DP-Image: Differential Privacy for Image Data in Feature Space

The excessive use of images in social networks, government databases, and industrial applications has posed great privacy risks and raised serious concerns from the public. Even though differential privacy (DP) is a widely accepted criterion that can provide a provable privacy guarantee, the application of DP on unstructured data such as images is not trivial due to the lack of a clear qualification on the meaningful difference between any two images. In this paper, for the first time, we introduce a novel notion of image-aware differential privacy, referred to as DP-image, that can protect user's personal information in images, from both human and AI adversaries. The DP-Image definition is formulated as an extended version of traditional differential privacy, considering the distance measurements between feature space vectors of images. Then we propose a mechanism to achieve DP-Image by adding noise to an image feature vector. Finally, we conduct experiments with a case study on face image privacy. Our results show that the proposed DP-Image method provides excellent DP protection on images, with a controllable distortion to faces

CAR: Conceptualization-Augmented Reasoner for Zero-Shot Commonsense Question Answering

The task of zero-shot commonsense question answering evaluates models on their capacity to reason about general scenarios beyond those presented in specific datasets. Existing approaches for tackling this task leverage external knowledge from CommonSense Knowledge Bases (CSKBs) by pretraining the model on synthetic QA pairs constructed from CSKBs. In these approaches, negative examples (distractors) are formulated by randomly sampling from CSKBs using fairly primitive keyword constraints. However, two bottlenecks limit these approaches: the inherent incompleteness of CSKBs limits the semantic coverage of synthetic QA pairs, and the lack of human annotations makes the sampled negative examples potentially uninformative and contradictory. To tackle these limitations above, we propose Conceptualization-Augmented Reasoner (CAR), a zero-shot commonsense question-answering framework that fully leverages the power of conceptualization. Specifically, CAR abstracts a commonsense knowledge triple to many higher-level instances, which increases the coverage of CSKB and expands the ground-truth answer space, reducing the likelihood of selecting false-negative distractors. Extensive experiments demonstrate that CAR more robustly generalizes to answering questions about zero-shot commonsense scenarios than existing methods, including large language models, such as GPT3.5 and ChatGPT. Our codes, data, and model checkpoints are available at https://github.com/HKUST-KnowComp/CAR

Discovery and Identification of Pyrazolopyramidine Analogs as Novel Potent Androgen Receptor Antagonists

Androgen receptor (AR), an important target in the current androgen derivation therapy, plays a critical role in the development and progress of prostate cancer (PCa). Nonsteroidal antiandrogens, such as enzalutamide and bicalutamide, are commonly used in clinic to treat PCa. Though they are very effective at the beginning, drug resistance problem appears after about 18 months. One of the reasons is that these antiandrogens share similar structure skeleton. Therefore, it is urgent to discover novel antiandrogens with different skeletons for resistance problem. Herein, we combined structure- and ligand-based methodologies for virtual screening chemical databases to identify potent AR antagonists. Then the cytotoxic activities of the screened hit samples were evaluated by using LNCaP prostate cancer cells. Virtual screening and biological evaluation assay results suggest that several chemicals with novel pyrazolopyrimidine skeleton can inhibit the proliferation of prostate cancer cells with similar, or even higher, bioactivities to bicalutamide. AR reporter gene assay experiments proved that Compound III showed potential antagonistic effects. In addition, molecular dynamics simulations results proved that Compound III can properly bind to AR and prevent helix 12 (H12) from closing to distort the formation of activation function 2 (AF2) site, resulting in the invalid transcription. Hence, pyrazolopyrimidine was discovered as a novel, potent and promising antiandrogen skeleton deserved to be further studied

Prevalence and associated factors of internet addiction among Chinese adolescents: association with childhood trauma

IntroductionInternet addiction (IA) is common among adolescents and may have severe consequences. This study aimed to investigate the prevalence and factors associated with IA among middle school students of Hunan Province, China. Relevance between IA and childhood trauma was also explored.MethodsOne thousand six hundred ten students were enrolled in this cross-sectional study. Data collected included demographics; internet addiction (revised-Chen internet addiction scale); childhood trauma (CTQ-SF); depression, anxiety, and stress symptoms (DASS-21); suicidal behaviors, as well as non-suicidal self-injury (NSSI). Cramer’s V analysis, univariable logistic regression and multivariable logistic regression were used for associations and identifying independent relevance of IA, respectively.ResultsThe prevalence of IA was 12.8%. Cramer’s V analysis showed that IA was associated with emotional abuse, emotional and physical neglect, NSSI, suicidal behaviors, stress, anxiety and depressive symptoms, physical disorder history. Regression analysis showed that IA was independently associated with emotional neglect (OR = 3.062, 95% CI: 2.083, 4.501, p < 0.001); physical neglect (OR = 2.328; 95% CI: 1.590, 3.409, p < 0.001); depressive symptoms (OR = 2.218, 95% CI: 1.467, 3.353, p < 0.001) nationality (OR = 1.888, 95% CI: 1.034, 3.447, p = 0.006) and age (OR = 1.253, 95% CI: 1.066, 1.471, p = 0.006).DiscussionIA is common among middle school students. Attention should be paid to students with childhood trauma since they have a higher risk for IA, which may increase the risk for suicidal behaviors

StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding

Analogy-making between narratives is crucial for human reasoning. In this paper, we evaluate the ability to identify and generate analogies by constructing a first-of-its-kind large-scale story-level analogy corpus, \textsc{StoryAnalogy}, which contains 24K story pairs from diverse domains with human annotations on two similarities from the extended Structure-Mapping Theory. We design a set of tests on \textsc{StoryAnalogy}, presenting the first evaluation of story-level analogy identification and generation. Interestingly, we find that the analogy identification tasks are incredibly difficult not only for sentence embedding models but also for the recent large language models (LLMs) such as ChatGPT and LLaMa. ChatGPT, for example, only achieved around 30% accuracy in multiple-choice questions (compared to over 85% accuracy for humans). Furthermore, we observe that the data in \textsc{StoryAnalogy} can improve the quality of analogy generation in LLMs, where a fine-tuned FlanT5-xxl model achieves comparable performance to zero-shot ChatGPT.Comment: Accepted by EMNLP 2023 main conferenc

Hsp20 Functions as a Novel Cardiokine in Promoting Angiogenesis via Activation of VEGFR2

Heat shock proteins (Hsps) are well appreciated as intrinsic protectors of cardiomyocytes against numerous stresses. Recent studies have indicated that Hsp20 (HspB6), a small heat shock protein, was increased in blood from cardiomyopathic hamsters. However, the exact source of the increased circulating Hsp20 and its potential role remain obscure. In this study, we observed that the circulating Hsp20 was increased in a transgenic mouse model with cardiac-specific overexpression of Hsp20, compared with wild-type mice, suggesting its origin from cardiomyocytes. Consistently, culture media harvested from Hsp20-overexpressing cardiomyocytes by Ad.Hsp20 infection contained an increased amount of Hsp20, compared to control media. Furthermore, we identified that Hsp20 was secreted through exosomes, independent of the endoplasmic reticulum-Golgi pathway. To investigate whether extracellular Hsp20 promotes angiogenesis, we treated human umbilical vein endothelial cells (HUVECs) with recombinant human Hsp20 protein, and observed that Hsp20 dose-dependently promoted HUVEC proliferation, migration and tube formation. Moreover, a protein binding assay and immunostaining revealed an interaction between Hsp20 and VEGFR2. Accordingly, stimulatory effects of Hsp20 on HUVECs were blocked by a VEGFR2 neutralizing antibody and CBO-P11 (a VEGFR inhibitor). These in vitro data are consistent with the in vivo findings that capillary density was significantly enhanced in Hsp20-overexpressing hearts, compared to non-transgenic hearts. Collectively, our findings demonstrate that Hsp20 serves as a novel cardiokine in regulating myocardial angiogenesis through activation of the VEGFR signaling cascade

Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization

Conceptualization, or viewing entities and situations as instances of abstract concepts in mind and making inferences based on that, is a vital component in human intelligence for commonsense reasoning. Although recent artificial intelligence has made progress in acquiring and modelling commonsense, attributed to large neural language models and commonsense knowledge graphs (CKGs), conceptualization is yet to thoroughly be introduced, making current approaches ineffective to cover knowledge about countless diverse entities and situations in the real world. To address the problem, we thoroughly study the possible role of conceptualization in commonsense reasoning, and formulate a framework to replicate human conceptual induction from acquiring abstract knowledge about abstract concepts. Aided by the taxonomy Probase, we develop tools for contextualized conceptualization on ATOMIC, a large-scale human annotated CKG. We annotate a dataset for the validity of conceptualizations for ATOMIC on both event and triple level, develop a series of heuristic rules based on linguistic features, and train a set of neural models, so as to generate and verify abstract knowledge. Based on these components, a pipeline to acquire abstract knowledge is built. A large abstract CKG upon ATOMIC is then induced, ready to be instantiated to infer about unseen entities or situations. Furthermore, experiments find directly augmenting data with abstract triples to be helpful in commonsense modelling.Comment: 36 pages, 11 figure

Induced pluripotent stem cells generated from human adipose-derived stem cells using a non-viral polycistronic plasmid in feeder-free conditions.

Induced pluripotent stem cells (iPSCs) can be generated from somatic cells by ectopic expression of defined transcription factors (TFs). However, the optimal cell type and the easy reprogramming approaches that minimize genetic aberrations of parent cells must be considered before generating the iPSCs. This paper reports a method to generate iPSCs from adult human adipose-derived stem cells (hADSCs) without the use of a feeder layer, by ectopic expression of the defined transcription factors OCT4, SOX2, KLF4 and C-MYC using a polycistronic plasmid. The results, based on the expression of pluripotent marker, demonstrated that the iPSCs have the characteristics similar to those of embryonic stem cells (ESCs). The iPSCs differentiated into three embryonic germ layers both in vitro by embryoid body generation and in vivo by teratoma formation after being injected into immunodeficient mice. More importantly, the plasmid DNA does not integrate into the genome of human iPSCs as revealed by Southern blotting experiments. Karyotypic analysis also demonstrated that the reprogramming of hADSCs by the defined factors did not induce chromosomal abnormalities. Therefore, this technology provides a platform for studying the biology of iPSCs without viral vectors, and can hopefully overcome immune rejection and ethical concerns, which are the two important barriers of ESC applications