27 papers · ranked by Valyu relevance
Javier Conde, María Grandury, Tairan Fu, Carlos Arriaga + 6 more
Word-level psycholinguistic norms are necessary to test theories of language processing. However, obtaining such human-based measures is not always feasible or straightforward. One promising approach is to augment human norming datasets by using large language models (LLMs) to predict these characteristics directly, a…
Cristina Gârbacea, Qiaozhu Mei
Recent advances in neural network-based generative modeling have reignited the hopes in having computer systems capable of seamlessly conversing with humans and able to understand natural language. Neural architectures have been employed to generate text excerpts to various degrees of success, in a multitude of…
Gustavo Henrique de Rosa, João Paulo Papa
This work presents a thorough review concerning recent studies and text generation advancements using Generative Adversarial Networks. The usage of adversarial learning for text generation is promising as it provides alternatives to generate the so-called "natural" language. Nevertheless, adversarial text generation is…
Rohit Pandey, Hetvi Waghela, Sneha Rakshit, Aparna Rangari + 4 more
A text generation model is a machine learning model that uses neural networks, especially transformers architecture to generate contextually relevant text based on linguistic patterns learned from extensive corpora. The models are trained on a huge amount of textual data so that they can model and learn complex…
Desta Haileselassie Hagos, Rick Battle, Danda B. Rawat
Status, Challenges, and Perspectives Authors: ['Desta Haileselassie Hagos' 'Rick Battle' 'Danda B. Rawat'] Abstract—The emergence of Generative Artificial Intelligence (AI) and Large Language Models (LLMs) has marked a new era of Natural Language Processing (NLP), introducing unprecedented capabilities that are…
Ece Uzun, Tiffany Leung, Sean Hacking
The generative artificial intelligence (AI) model ChatGPT holds transformative prospects in medicine. The development of such models has signaled the beginning of a new era where complex biological data can be made more accessible and interpretable. ChatGPT is a natural language processing tool that can process…
Robert Verkuil, Ori Kabeli, Yilun Du, Basile I. M. Wicky + 6 more
Learning the design patterns of proteins from sequences across evolution may have promise toward generative protein design. However it is unknown whether language models, trained on sequences of natural proteins, will be capable of more than memorization of existing protein families. Here we show that language models…
Justin Sybrandt, Ilya Safro, Friedhelm Schwenker
Biomedical research papers often combine disjoint concepts in novel ways, such as when describing a newly discovered relationship between an understudied gene with an important disease. These concepts are often explicitly encoded as metadata keywords, such as the author-provided terms included with many documents in…
Elena Lloret, Anabela Barreiro, Mehul Bhatt, Alberto Bugarín-Diz + 9 more
'Gianfranco E. Modoni' 'Max Silberztein' 'Iacer Calixto' 'Grazina Korvel' 'Konstantinos Diamantaras' 'Alkiviadis Katsalis' 'Oleksii Turuta' 'Irene Russo' 'Aykut Erdem'] The article emphasizes the critical importance of language generation today, particularly focusing on three key aspects: Multitasking, Multilinguality…
Sanjar Adilov
Generative neural networks have shown promising results in de novo drug design. Recent studies suggest that one of the efficient ways to produce novel molecules matching target properties is to model SMILES sequences using deep learning in a way similar to language modeling in natural language processing. In this…
Sashank Santhanam, Samira Shaikh
One of the hardest problems in the area of Natural Language Processing and Artificial Intelligence is automatically generating language that is coherent and understandable to humans. Teaching machines how to converse as humans do falls under the broad umbrella of Natural Language Generation. Recent years have seen an…
Kilian Carolan, Laura Fennelly, Alan F. Smeaton
Large Language Models (LLMs) have recently emerged as a focal point of research and application, driven by their unprecedented ability to understand and generate text with human-like quality. Even more recently, LLMs have been extended into multi-modal large language models (MM-LLMs) which extends their capabilities to…
Bin Shao
Generative pre-trained transformers (GPTs) have revolutionized the field of natural language processing. Inspired by this success, we develop a long-context generative model for genomes. Our multiscale transformer model was pre-trained on unannotated bacteriophage genomes with byte-level tokenization. It generates de…
Ran He, Jie Cao, Tieniu Tan
Generative artificial intelligence (GAI) has recently achieved significant success, enabling anyone to create texts, images, videos and even computer codes while providing insights that might not be possible with traditional tools. To stimulate future research, this work provides a brief summary of the ongoing and…
Enes Avcu, David Gow
Human cognitive and linguistic generativity depends on the ability to identify abstract relationships between perceptually dissimilar items. 36 found that human infants can rapidly discover and generalize patterns of syllable repetition (reduplication) that depend on the abstract property of identity, but simple…
Mhd Hussein Murtada, Z. Faidon Brotzakis, Michele Vendruscolo
Molecular Dynamics (MD) simulations provide accurate descriptions of the motions of molecular systems, yet their computational demands pose significant challenges in applications in molecular biology and materials science. Given the success of deep learning methods in a wide range of fields, a timely question concerns…
Alexander M. Ille, Christopher Markosian, Stephen K. Burley, Michael B. Mathews + 2 more
Natural language-based generative artificial intelligence (AI) has become increasingly prevalent in scientific research. Intriguingly, capabilities of generative pre-trained transformer (GPT) language models beyond the scope of natural language tasks have recently been identified. Here we explored how GPT-4 might be…
Authors not listed
Language models have been increasingly popular in therapeutic peptide generation, but molecular diversity remains limited due to reliance on the 20 canonical amino acids. We propose a language model that generates peptidomimetics incorporating non-canonical elements like non-canonical amino acids and terminal…
Authors not listed
The ability to generate crystal structures directly from textual descriptions marks a pivotal advancement in materials informatics and underscores the emerging role of large language models (LLMs) in inverse design. In this work, we introduce CrysText, a text-conditioned framework that generates crystal structures in…
Authors not listed
Here we use a large language model for the exploration of chemical space of transition metal complexes (TMCs), fine-tuning the open-source Llama-3.2-1B model with a variation of the SMILES string representation tailored for coordination chemistry. We identify several key molecular properties that are critical to…
Shuya Nakata, Yoshiharu Mori, Shigenori Tanaka
Ultra-large virtual chemical spaces have emerged as a valuable resource for drug discovery, providing access to billions of make-on-demand compounds with high synthetic success rates. Chemical language models can potentially accelerate the exploration of these vast spaces through direct compound generation. However…
Nathan Frey, Ryan Soklaski, Simon Axelrod, Siddharth Samsi + 3 more
Massive scale, both in terms of data availability and computation, enables significant breakthroughs in key application areas of deep learning such as natural language processing (NLP) and computer vision. There is emerging evidence that scale may be a key ingredient in scientific deep learning, but the importance of…
Michael Moret, Lukas Friedrich, Francesca Grisoni, Daniel Merk + 1 more
Generative machine learning models sample drug-like molecules from chemical space without the need for explicit design rules. A deep learning framework for customized compound library generation is presented, aiming to enrich and expand the pharmacologically relevant chemical space with new molecular entities ‘on…
Kenji Sagae
Recent work on the application of neural networks to language modeling has shown that models based on certain neural architectures can capture syntactic information from utterances and sentences even when not given an explicitly syntactic objective. We examine whether a fully data-driven model of language development…
Kristijan Armeni, Roel M. Willems, Stefan Frank
Cognitive neuroscientists of language comprehension study how neural computations relate to cognitive computations during comprehension. On the cognitive part of the equation, it is important that the computations and processing complexity are explicitly defined. Probabilistic language models can be used to give a…
Dongqiu Zhang, Wenkui Li
Natural Language Understanding (NLU) and Natural Language Generation (NLG) are the general methods that support machine understanding of text content. They play a very important role in the text information processing system including recommendation and question and answer systems. There are many researches in the…
Nima Hadidi, Ebrahim Feghhi, Bryan H. Song, Idan A. Blank + 1 more
Emerging research seeks to draw neuroscientific insights from the neural predictivity of large language models (LLMs). However, as results continue to be generated at a rapid pace, there is a growing need for large-scale assessments of their robustness. Here, we analyze a wide range of models, methodological…