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

Data-centric AI has shed light on the significance of data within the machine learning (ML) pipeline. Recognizing its significance, academia, industry, and government departments have suggested various NLP data research initiatives. While the ability to utilize existing data is essential, the ability to build a dataset has become more critical than ever, especially in the industry. In consideration of this trend, we propose a"Data Management Operations and Recipes"to guide the industry in optimizing the building of datasets for NLP products. This paper presents the concept of DMOps which is derived from real-world experiences with NLP data management and aims to streamline data operations by offering a baseline.

DMOps: Data Management Operation and Recipes

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

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

Doubts on the reliability of parallel corpus filtering

Length-aware Byte Pair Encoding for Mitigating Over-segmentation in Korean Machine Translation

The evolution of large language models (LLMs) has culminated in a multitask model paradigm where prompts drive the generation of user-specific outputs. However, this advancement has revealed a challenge: LLMs frequently produce outputs against socially acceptable commonsense standards in various scenarios. To address this gap in commonsense reasoning, we present KoCommonGEN v2, a fine-grained benchmark dataset focused on Korean common-sense reasoning. This dataset, enriched with human annotations, comprises multiple-choice questions across seven error categories. These categories include commonsense memorization, numerical commonsense, toxic speech, and more, which are vulnerable to undermining the reliability of LLMs’ commonsense reasoning capabilities. The empirical results present that LLMs struggle with Korean commonsense reasoning. With human accuracy benchmarked at approximately 85%, GPT-4’s performance lags at about 74%, and other LLMs demonstrate an average accuracy of around 42%. Our findings emphasize the need for targeted improvements in Korean commonsense reasoning within LLMs, paving the way for more socially and contextually sensitive AI models. KoCom-monGEN v2 is one of the benchmark datasets for the Open Ko-LLM Leaderboard.

KoCommonGEN v2: A Benchmark for Navigating Korean Commonsense Reasoning Challenges in Large Language Models

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Seungjun Lee

Author Tools

Chat about Author