There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Large Language Models (LLMs) have drawn a lot of attention due to their strong performance on a wide range of natural language tasks, since the release of ChatGPT in November 2022. LLMs' ability of general-purpose language understanding and generation is acquired by training billions of model's parameters on massive amounts of text data, as predicted by scaling laws \cite{kaplan2020scaling,hoffmann2022training}. The research area of LLMs, while very recent, is evolving rapidly in many different ways. In this paper, we review some of the most prominent LLMs, including three popular LLM families (GPT, LLaMA, PaLM), and discuss their characteristics, contributions and limitations. We also give an overview of techniques developed to build, and augment LLMs. We then survey popular datasets prepared for LLM training, fine-tuning, and evaluation, review widely used LLM evaluation metrics, and compare the performance of several popular LLMs on a set of representative benchmarks. Finally, we conclude the paper by discussing open challenges and future research directions.

Large Language Models: A Survey

The widespread propagation of misinformation on social media platforms poses a significant concern, prompting substantial endeavors within the research community to develop robust detection solutions. Individuals often place unwavering trust in social networks, often without discerning the origins and authenticity of the information disseminated through these platforms. Hence, the identification of media-rich fake news necessitates an approach that adeptly leverages multimedia elements and effectively enhances detection accuracy. The ever-changing nature of cyberspace highlights the need for measures that may effectively resist the spread of media-rich fake news while protecting the integrity of information systems. This study introduces a robust approach for fake news detection, utilizing three publicly available datasets: WELFake, FakeNewsNet, and FakeNewsPrediction. We integrated FastText word embeddings with various Machine Learning and Deep Learning methods, further refining these algorithms with regularization and hyperparameter optimization to mitigate overfitting and promote model generalization. Notably, a hybrid model combining Convolutional Neural Networks and Long Short-Term Memory, enriched with FastText embeddings, surpassed other techniques in classification performance across all datasets, registering accuracy and F1-scores of 0.99, 0.97, and 0.99, respectively. Additionally, we utilized state-of-the-art transformer-based models such as BERT, XLNet, and RoBERTa, enhancing them through hyperparameter adjustments. These transformer models, surpassing traditional RNN-based frameworks, excel in managing syntactic nuances, thus aiding in semantic interpretation. In the concluding phase, explainable AI modeling was employed using Local Interpretable Model-Agnostic Explanations, and Latent Dirichlet Allocation to gain deeper insights into the model's decision-making process. 

Advancing Fake News Detection: Hybrid Deep Learning With FastText and Explainable AI

Abstract Background Language learners are taking advantage of the increased accuracy afforded by machine translation (MT) tools like Google Translate. There is a growing debate as to whether these tools support or hinder English as a foreign language (EFL) education. Some EFL students are drawn to MT tools to save time and energy when communicating, but how this use relates to second language acquisition needs to be clarified. Objective This study investigated the perceptions and practices of MT tools among university EFL students in Saudi Arabia and South Korea. Methods The study surveyed Saudi ( n = 310) and South Korean ( n = 160) university students to explore the use of MT tools in EFL learning. It used mean scores, correlation analysis, and structural equation modelling to understand the perceived benefits and limitations of MT tools and their influence on the use of translation websites. The findings were triangulated using content analysis of open‐ended items. Results and Conclusion Findings indicate students showed a high level of acceptance and utilization of MT tools. English avoidance behaviour ( B = 0.43), benefits ( B = 0.32), and foreign language proficiency ( B = −0.19) predicted 61% of actual use of MT tools like Google Translate. Further, students frequently used MT tools despite the reported concerns about accuracy mistakes. Saudi students listed a more comprehensive array of why they use MT tools than Korean students, while Saudis and Koreans frequently used MT tools for individual word and sentence translations. Implications Students should edit MT output when writing and are encouraged to use the voice recording and other functions of MT tools to learn language skills. Instructors must prevent overuse and dependency on MT tools, especially among low‐proficiency students who want to avoid using English. 

Machine translation in language acquisition: A study on EFL students' perceptions and practices in Saudi Arabia and South Korea

Identifying the gender of a person and his age by way of speaking is considered a crucial task in computer vision. It is a very important and active research topic with many areas of application, such as identifying a person, trustworthiness, demographic analysis, safety and health knowledge, visual monitoring, and aging progress. Data matching is to identify the gender of the person and his age. Thus, the study touches on a review of many research papers from 2016 to 2022. At the heart of the topic, many systematic reviews of multimodal pedagogies in Age and Gender Estimation for Adaptive were undertaken. However, no current study of the theme concerns connected to multimodal pedagogies in Age and Gender Estimation for Adaptive Learning has been published. The multimodal pedagogies in four different databases within the keywords indicate the heart of the topic. A qualitative thematic analysis based on 48 articles found during the search revealed four common themes, such as multimodal engagement and speech with the Human-Robot Interaction life world. The study touches on the presentation of many major concepts, namely Age Estimation, Gender Estimation, Speaker Recognition, Speech recognition, Speaker Localization, and Speaker Gender Identification. According to specific criteria, they were presented to all studies. The essay compares these themes to the thematic findings of other review studies on the same topic such as multimodal age, gender estimation, and dataset used. The main objective of this paper is to provide a comprehensive analysis based on the surveyed region. The study provides a platform for professors, researchers, and students alike, and proposes directions for future research.

/pdf/multimodal-age-and-gender-estimation-for-adaptive-human-2d129q8s.pdf

Multimodal Age and Gender Estimation for Adaptive Human-Robot Interaction: A Systematic Literature Review

The success of Transformer architecture has seen increased interest in machine translation (MT). The translation quality of neural network-based MT transcends that of translations derived using statistical methods. This growth in MT research has entailed the development of accurate automatic evaluation metrics that allow us to track the performance of MT. However, automatically evaluating and comparing MT systems is a challenging task. Several studies have shown that traditional metrics (e.g., BLEU, TER) show poor performance in capturing semantic similarity between MT outputs and human reference translations. To date, to improve performance, various evaluation metrics have been proposed using the Transformer architecture. However, a systematic and comprehensive literature review on these metrics is still missing. Therefore, it is necessary to survey the existing automatic evaluation metrics of MT to enable both established and new researchers to quickly understand the trend of MT evaluation over the past few years. In this survey, we present the trend of automatic evaluation metrics. To better understand the developments in the field, we provide the taxonomy of the automatic evaluation metrics. Then, we explain the key contributions and shortcomings of the metrics. In addition, we select the representative metrics from the taxonomy, and conduct experiments to analyze related problems. Finally, we discuss the limitation of the current automatic metric studies through the experimentation and our suggestions for further research to improve the automatic evaluation metrics.

/pdf/a-survey-on-evaluation-metrics-for-machine-translation-2vypl9cc.pdf

A Survey on Evaluation Metrics for Machine Translation

Most bowel intussusceptions in adults have a leading point. However, there have been few reports of jejunojejunal intussusception secondary to a solitary juvenile polyp in adult. We report herein the case of a 19-year-old female with a solitary juvenile polyp in the jejunum causing intussusception. Laparoscopic-assisted reduction and segmental resection of the jejunum were successfully done for the patient.

/pdf/laparoscopic-assisted-resection-of-jejunojejunal-95h1owqrij.pdf

Laparoscopic-assisted resection of jejunojejunal intussusception caused by a juvenile polyp in an adult.

Recently, active research has been conducted on Korean grammatical error correction on machine translation (MT) and automatic noise generation. However, there is no gold-standard test set for objective and official comparative analysis. A significant limitation is measuring the ill-defined performance because the experimental error types in the train set are also included in the test set. Moreover, error types in the training set are also included in the test set. Additionally, the types of errors for qualitative analysis are defined differently with no explicit guidelines. This study proposes a gold-standard test set called the Korean Neural Grammatical Correction Test set (K-NCT) for Korean grammatical error correction using a new error type classification guideline. To ensure the factuality and reliability of the proposal, we conduct a quantitative analysis using a commercialization system and human evaluation. Experimental results demonstrate that the proposed grammatical error correction test set has a well-balanced, diverse, and precise guideline. Our dataset is available at https://github.com/seonminkoo/K-NCT

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09938990.pdf

K-NCT: Korean Neural Grammatical Error Correction Gold-Standard Test Set Using Novel Error Type Classification Criteria

BlenderBot 2.0 is a dialogue model that represents open-domain chatbots by reﬂecting real-time information and remembering user information for an extended period using an internet search module and multi-session. Nonetheless, the model still has room for improvement. To this end, we examined BlenderBot 2.0’s limitations and errors from three perspectives: model, data, and user. From the data point of view, we highlight the unclear guidelines provided to workers during the crowdsourcing process, as well as a lack of a process for re-ﬁning hate speech in the collected data and verifying the accuracy of internet-based information. From a user’s perspective, we identify nine types of problems of BlenderBot 2.0, and their causes are thoroughly investigated. Furthermore, for each point of view, practical improvement methods are proposed, and we discuss several potential future research direc-tions.

Empirical study on BlenderBot 2.0 Errors Analysis in terms of Model, Data and User-Centric Approach

With the recent advance in neural machine translation demonstrating its importance, research on quality estimation (QE) has been steadily progressing. QE aims to automatically predict the quality of machine translation (MT) output without reference sentences. Despite its high utility in the real world, there remain several limitations concerning manual QE data creation: inevitably incurred non-trivial costs due to the need for translation experts, and issues with data scaling and language expansion. To tackle these limitations, we present QUAK, a Korean-English synthetic QE dataset generated in a fully automatic manner. This consists of three sub-QUAK datasets QUAK-M, QUAK-P, and QUAK-H, produced through three strategies that are relatively free from language constraints. Since each strategy requires no human effort, which facilitates scalability, we scale our data up to 1.58M for QUAK-P, H and 6.58M for QUAK-M. As an experiment, we quantitatively analyze word-level QE results in various ways while performing statistical analysis. Moreover, we show that datasets scaled in an efficient way also contribute to performance improvements by observing meaningful performance gains in QUAK-M, P when adding data up to 1.58M.

QUAK: A Synthetic Quality Estimation Dataset for Korean-English Neural Machine Translation

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