Natural Language Processing (NLP) and Large Language Modelling

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: 30 November 2024 | Viewed by 1420

Special Issue Editor


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Guest Editor
School of Info Technology, Faculty of Science, Engineering and Built Environment, Geelong Waurn Ponds Campus, Deakin University, Geelong, VIC 3216, Australia
Interests: natural language processing; small efficient language modelling; continual learning; text generation; adversarial learning; scientific text mining; multimodality; conversational systems

Special Issue Information

Dear Colleagues,

NLP is a rapidly evolving field that plays a crucial role in shaping the future of human–computer interactions, with applications ranging from sentiment analysis and machine translation to question answering and dialogue systems.

We invite researchers, practitioners, and enthusiasts to submit original research articles, reviews, and case studies that contribute to the advancement of NLP. Extended conference papers are also welcome, but they should contain at least 50% of new material, e.g., in the form of technical extensions, more in-depth evaluations, or additional use cases. Topics of interest for this Special Issue include, but are not limited to, the following:

  • Large language modelling and its applications;
  • Sentiment analysis and opinion mining;
  • Machine translation and multilingual processing;
  • Question answering and information retrieval;
  • Dialogue systems and conversational agents;
  • Text summarization and generation;
  • Natural language understanding and generation;
  • NLP applications in healthcare, finance, education, and other domains.

Submissions should present novel research findings, innovative methodologies, and practical applications that demonstrate the current state of the art in NLP. We welcome interdisciplinary approaches and encourage submissions that explore the intersection of NLP with other fields, such as machine learning, artificial intelligence, and cognitive science.

Dr. Ming Liu
Guest Editor

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Keywords

  • natural language processing
  • small efficient language modelling
  • continual learning
  • text generation
  • adversarial learning
  • scientific text mining
  • multimodality
  • conversational systems
  • large language model

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Published Papers (2 papers)

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Research

31 pages, 2905 KiB  
Article
On Using GeoGebra and ChatGPT for Geometric Discovery
by Francisco Botana, Tomas Recio and María Pilar Vélez
Computers 2024, 13(8), 187; https://doi.org/10.3390/computers13080187 - 30 Jul 2024
Viewed by 404
Abstract
This paper explores the performance of ChatGPT and GeoGebra Discovery when dealing with automatic geometric reasoning and discovery. The emergence of Large Language Models has attracted considerable attention in mathematics, among other fields where intelligence should be present. We revisit a couple of [...] Read more.
This paper explores the performance of ChatGPT and GeoGebra Discovery when dealing with automatic geometric reasoning and discovery. The emergence of Large Language Models has attracted considerable attention in mathematics, among other fields where intelligence should be present. We revisit a couple of elementary Euclidean geometry theorems discussed in the birth of Artificial Intelligence and a non-trivial inequality concerning triangles. GeoGebra succeeds in proving all these selected examples, while ChatGPT fails in one case. Our thesis is that both GeoGebra and ChatGPT could be used as complementary systems, where the natural language abilities of ChatGPT and the certified computer algebra methods in GeoGebra Discovery can cooperate in order to obtain sound and—more relevant—interesting results. Full article
(This article belongs to the Special Issue Natural Language Processing (NLP) and Large Language Modelling)
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24 pages, 578 KiB  
Article
An NLP-Based Exploration of Variance in Student Writing and Syntax: Implications for Automated Writing Evaluation
by Maria Goldshtein, Amin G. Alhashim and Rod D. Roscoe
Computers 2024, 13(7), 160; https://doi.org/10.3390/computers13070160 - 25 Jun 2024
Viewed by 636
Abstract
In writing assessment, expert human evaluators ideally judge individual essays with attention to variance among writers’ syntactic patterns. There are many ways to compose text successfully or less successfully. For automated writing evaluation (AWE) systems to provide accurate assessment and relevant feedback, they [...] Read more.
In writing assessment, expert human evaluators ideally judge individual essays with attention to variance among writers’ syntactic patterns. There are many ways to compose text successfully or less successfully. For automated writing evaluation (AWE) systems to provide accurate assessment and relevant feedback, they must be able to consider similar kinds of variance. The current study employed natural language processing (NLP) to explore variance in syntactic complexity and sophistication across clusters characterized in a large corpus (n = 36,207) of middle school and high school argumentative essays. Using NLP tools, k-means clustering, and discriminant function analysis (DFA), we observed that student writers employed four distinct syntactic patterns: (1) familiar and descriptive language, (2) consistently simple noun phrases, (3) variably complex noun phrases, and (4) moderate complexity with less familiar language. Importantly, each pattern spanned the full range of writing quality; there were no syntactic patterns consistently evaluated as “good” or “bad”. These findings support the need for nuanced approaches in automated writing assessment while informing ways that AWE can participate in that process. Future AWE research can and should explore similar variability across other detectable elements of writing (e.g., vocabulary, cohesion, discursive cues, and sentiment) via diverse modeling methods. Full article
(This article belongs to the Special Issue Natural Language Processing (NLP) and Large Language Modelling)
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