Are Large Language Models Good Fact Checkers: A Preliminary Study

Recently, Large Language Models (LLMs) have drawn significant attention due to their outstanding reasoning capabilities and extensive knowledge repository, positioning them as superior in handling various natural language processing tasks compared to other language models. In this paper, we present a preliminary investigation into the potential of LLMs in fact-checking. This study aims to comprehensively evaluate various LLMs in tackling specific fact-checking subtasks, systematically evaluating their capabilities, and conducting a comparative analysis of their performance against pre-trained and state-of-the-art low-parameter models. Experiments demonstrate that LLMs achieve competitive performance compared to other small models in most scenarios. However, they encounter challenges in effectively handling Chinese fact verification and the entirety of the fact-checking pipeline due to language inconsistencies and hallucinations. These findings underscore the need for further exploration and research to enhance the proficiency of LLMs as reliable fact-checkers, unveiling the potential capability of LLMs and the possible challenges in fact-checking tasks

UCAS-IIE-NLP at SemEval-2023 Task 12: Enhancing Generalization of Multilingual BERT for Low-resource Sentiment Analysis

This paper describes our system designed for SemEval-2023 Task 12: Sentiment analysis for African languages. The challenge faced by this task is the scarcity of labeled data and linguistic resources in low-resource settings. To alleviate these, we propose a generalized multilingual system SACL-XLMR for sentiment analysis on low-resource languages. Specifically, we design a lexicon-based multilingual BERT to facilitate language adaptation and sentiment-aware representation learning. Besides, we apply a supervised adversarial contrastive learning technique to learn sentiment-spread structured representations and enhance model generalization. Our system achieved competitive results, largely outperforming baselines on both multilingual and zero-shot sentiment classification subtasks. Notably, the system obtained the 1st rank on the zero-shot classification subtask in the official ranking. Extensive experiments demonstrate the effectiveness of our system.Comment: 9 pages, accepted by SemEval@ACL 202

Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations

Extracting generalized and robust representations is a major challenge in emotion recognition in conversations (ERC). To address this, we propose a supervised adversarial contrastive learning (SACL) framework for learning class-spread structured representations. The framework applies contrast-aware adversarial training to generate worst-case samples and uses a joint class-spread contrastive learning objective on both original and adversarial samples. It can effectively utilize label-level feature consistency and retain fine-grained intra-class features. To avoid the negative impact of adversarial perturbations on context-dependent data, we design a contextual adversarial training strategy to learn more diverse features from context and enhance the model's context robustness. We develop a sequence-based method SACL-LSTM under this framework, to learn label-consistent and context-robust emotional features for ERC. Experiments on three datasets demonstrate that SACL-LSTM achieves state-of-the-art performance on ERC. Extended experiments prove the effectiveness of the SACL framework.Comment: 16 pages, accepted by ACL 202

Hierarchical Interaction Networks with Rethinking Mechanism for Document-level Sentiment Analysis

Document-level Sentiment Analysis (DSA) is more challenging due to vague semantic links and complicate sentiment information. Recent works have been devoted to leveraging text summarization and have achieved promising results. However, these summarization-based methods did not take full advantage of the summary including ignoring the inherent interactions between the summary and document. As a result, they limited the representation to express major points in the document, which is highly indicative of the key sentiment. In this paper, we study how to effectively generate a discriminative representation with explicit subject patterns and sentiment contexts for DSA. A Hierarchical Interaction Networks (HIN) is proposed to explore bidirectional interactions between the summary and document at multiple granularities and learn subject-oriented document representations for sentiment classification. Furthermore, we design a Sentiment-based Rethinking mechanism (SR) by refining the HIN with sentiment label information to learn a more sentiment-aware document representation. We extensively evaluate our proposed models on three public datasets. The experimental results consistently demonstrate the effectiveness of our proposed models and show that HIN-SR outperforms various state-of-the-art methods.Comment: 17 pages, accepted by ECML-PKDD 202

Macrophyte identity shapes water column and sediment bacterial community

By assembling mesocosms and utilizing high-throughput sequencing, we aim to characterize the shifts of the bacterial community in freshwaters driven by two contrasting submerged macrophyte species, Ceratophyllum demersum L. and Vallisneria spiralis L. Although the microbe in both the water column and sediment were largely modulated by the macrophyte, the effect varied considerably depending on bacterial locations and macrophyte species. Actinobacteria was the most abundant taxa in the water column of all the three treatments, but its abundances were significantly higher in the two planted treatments. Moreover, Alphaproteobacteria showed high abundance only in the unplanted control. For bacterial taxa in the sediment, C. demersum significantly increased the relative abundance of Anaerolineae but reduced the relative abundance of Betaproteobacteria and Gammaproteobacteria, while V. spiralis increased the relative abundance of Deltaproteobacteria and Gammaproteobacteria. Additionally, in the C. demersum treatment, the water column bacterial community increased more dramatically in richness, alpha diversity, and the relative abundance of the dominant taxa than those in the V. spiralis treatment. Taken together, the findings from this study reveal that the two species of submerged macrophyte modified the bacterial community in waters, despite the obvious interspecific performance differences

GWO-BP neural network based OP performance prediction for mobile multiuser communication networks

The complexity and variability of wireless channels makes reliable mobile multiuser communications challenging. As a consequence, research on mobile multiuser communication networks has increased significantly in recent years. The outage probability (OP) is commonly employed to evaluate the performance of these networks. In this paper, exact closed-form OP expressions are derived and an OP prediction algorithm is presented. Monte-Carlo simulation is used to evaluate the OP performance and verify the analysis. Then, a grey wolf optimization back-propagation (GWO-BP) neural network based OP performance prediction algorithm is proposed. Theoretical results are used to generate training data. We also examine the extreme learning machine (ELM), locally weighted linear regression (LWLR), support vector machine (SVM), BP neural network, and wavelet neural network methods. Compared to the wavelet neural network, LWLR, SVM, BP, and ELM methods, the results obtained show that the GWO-BP method provides the best OP performance prediction

A Wohlfahrtiimonas chitiniclastica with a novel type of blaVEB–1-carrying plasmid isolated from a zebra in China

BackgroundWohlfahrtiimonas chitiniclastica is an emerging fly-borne zoonotic pathogen, which causes infections in immunocompromised patients and some animals. Herein, we reported a W. chitiniclastica BM-Y from a dead zebra in China.MethodsThe complete genome sequencing of BM-Y showed that this isolate carried one chromosome and one novel type of blaVEB–1-carrying plasmid. Detailed genetic dissection was applied to this plasmid to display the genetic environment of blaVEB–1.ResultsThree novel insertion sequence (IS) elements, namely ISWoch1, ISWoch2, and ISWoch3, were found in this plasmid. aadB, aacA1, and gcuG were located downstream of blaVEB–1, composing a gene cassette array blaVEB–1–aadB–aacA1–gcuG bracketed by an intact ISWoch1 and a truncated one, which was named the blaVEB–1 region. The 5′-RACE experiments revealed that the transcription start site of the blaVEB–1 region was located in the intact ISWoch1 and this IS provided a strong promoter for the blaVEB–1 region.ConclusionThe spread of the blaVEB–1-carrying plasmid might enhance the ability of W. chitiniclastica to survive under drug selection pressure and aggravate the difficulty in treating infections caused by blaVEB–1-carrying W. chitiniclastica. To the best of our knowledge, this is the first report of the genetic characterization of a novel blaVEB–1-carrying plasmid with new ISs from W. chitiniclastica