Search · four archives
Search · four archives
27 papers · ranked by Valyu relevance
Shervin Minaee, Tomas Mikolov, Narjes Nikzad-Khasmakhi, Meysam Chenaghlu + 3 more
'Meysam Chenaghlu' 'Richard Socher' 'Xavier Amatriain' 'Jianfeng Gao'] Abstract—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…
Arkaitz Zubiaga
Since their inception in the 1980s, language models (LMs) have been around for more than four decades as a means for statistically modeling the properties observed from natural language (Rosenfeld, ). Given a collection of texts as input, a language model computes statistical properties of language from those texts…
Weina Ke, Rui He, Mark A. Jensen, Marina A. Dobrovolskaia
The advances in cancer nanotechnologies and current efforts focused on data sharing, along with the associated challenges, have been summarized in the first part of this review. Herein, we explore the potential of Large Language Models (LLMs) to enhance user experience, using the federally funded data repository…
Giuseppe Sartori, Graziella Orrù
Large language models (LLMs) are demonstrating impressive performance on many reasoning and problem-solving tasks from cognitive psychology. When tested, their accuracy is often on par with average neurotypical adults, challenging long-standing critiques of associative models. Here we analyse recent findings at the…
Gonzalo Martínez, Javier Conde, Elena Merino-Gómez, Beatriz Bermúdez-Margaretto + 4 more
'Beatriz Bermúdez-Margaretto' 'José Alberto Hernández' 'Pedro Reviriego' 'Marc Brysbaert' 'Jessie S. Barrot'] Vocabulary tests, once a cornerstone of language modeling evaluation, have been largely overlooked in the current landscape of Large Language Models (LLMs) like Llama 2, Mistral, and GPT. While most LLM…
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…
Michael R. Douglas
Artificial intelligence is making spectacular progress, and one of the best examples is the development of large language models (LLMs) such as OpenAI's GPT series. In these lectures, written for readers with a background in mathematics or physics, we give a brief history and survey of the state of the art, and…
Zhuoqiao Hong, Haocheng Wang, Zaid Zada, Harshvardhan Gazula + 12 more
Recent research has used large language models (LLMs) to study the neural basis of naturalistic language processing in the human brain. LLMs have rapidly grown in complexity, leading to improved language processing capabilities. However, neuroscience researchers haven’t kept up with the quick progress in LLM…
Abeed Sarker, Rui Zhang, Yanshan Wang, Yunyu Xiao + 5 more
'Dalton Schutte' 'David Oniani' 'Qianqian Xie' 'Hua Xu'] Title: Summary Objectives : Large language models (LLMs) are revolutionizing the natural language pro-cessing (NLP) landscape within healthcare, prompting the need to synthesize the latest ad-vancements and their diverse medical applications. We attempt to…
Murray Shanahan
Thanks to rapid progress in artificial intelligence, we have entered an era when technology and philosophy intersect in interesting ways. Sitting squarely at the centre of this intersection are large language models (LLMs). The more adept LLMs become at mimicking human language, the more vulnerable we become to…
Zhibo Chu, Shiwen Ni, Zichong Wang, Xi Feng + 5 more
Introductory Survey Authors: ['Zhibo Chu' 'Shiwen Ni' 'Zichong Wang' 'Xi Feng' 'Chengming Li' 'Xiping Hu' 'Ruifeng Xu' 'Min Yang' 'Wenbin Zhang'] Language models serve as a cornerstone in natural language processing (NLP), utilizing mathematical methods to generalize language laws and knowledge for prediction and…
Jian Shen, Shengmin Zhou, Xing Che
The emergence of ChatGPT has drawn significant attention to Large Language Models (LLMs) due to their impressive performance. While LLMs primarily focus on next token/word prediction, we apply this principle to molecular design by reframing the task as predicting the next token/fragment. We present FragLlama, a large…
Jack Grieve, Sara Bartl, Matteo Fuoli, Jason Grafmiller + 6 more
In this article, we introduce a sociolinguistic perspective on language modeling. We claim that language models in general are inherently modeling varieties of language, and we consider how this insight can inform the development and deployment of language models. We begin by presenting a technical definition of the…
Robert H. Tai, Lillian R. Bentley, Xin Xia, Jason M. Sitt + 3 more
The increasing use of machine learning and Large Language Models (LLMs) opens up opportunities to use these artificially intelligent algorithms in novel ways. This article proposes a methodology using LLMs to support traditional deductive coding in qualitative research. We began our analysis with three different sample…
Sofia Serrano, Zander Brumbaugh, Noah A. Smith
| 1 | Introduction | | 2 | | --- | --- | --- | --- | | 2 | | Background: Natural language processing concepts and tools | 3 | | | 2.1 | Taskification: Defining what we want a system to do | 4 | | | | 2.1.1 Abstract vs. concrete system capabilities | 4 | | | | 2.1.2 We need data and an evaluation method for research…
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…
Changjiang Gao, Zhengwu Ma, Jiajun Chen, Ping Li + 2 more
Transformer-based large language models (LLMs) have significantly advanced our understanding of meaning representation in the human brain. However, increasingly large LLMs have been questioned as valid cognitive models due to their extensive training data and their ability to access context thousands of words long. In…
Mahrad Almotahari
Cooperative speech is purposive. From the speaker's perspective, one crucial purpose is the transmission of knowledge. Cooperative speakers care about getting things right for their conversational partners. This attitude is a kind of respect. Cooperative speech is an ideal form of communication because participants…
Authors not listed
Step-by-step thinking is essential in all domains of chemical sciences and engineering. While machine learning tools are broadly used, algorithms that automate reasoning are far less common. We elaborate on seven categories of human reasoning activities and connect each to applications in chemical science and…
Kevin Maik Jablonka, Philippe Schwaller, Andres Ortega-Guerrero, Berend Smit
Machine learning has revolutionized many fields and has recently found applications in chemistry and materials science. The small datasets commonly found in chemistry lead to various sophisticated machine-learning approaches that incorporate chemical knowledge for each application and therefore require a lot of…
Adrielli Lopes Rego, Joshua Snell, Martijn Meeter
Although word predictability is commonly considered an important factor in reading, sophisticated accounts of predictability in theories of reading are yet lacking. Computational models of reading traditionally use cloze norming as a proxy of word predictability, but what cloze norms precisely capture remains unclear.…
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…
Authors not listed
Accelerating computational materials science relies not only on hardware advances but also on software that increases the ease of working with the relevant abstractions. Creation and manipulation of crystal structures is a part of many routine materials science workflows. In this work, we demonstrate how fine tuning…
Ayan Sengupta, Vaibhav Seth, Ashok Kumar Pathak, Natraj Raman + 2 more
Reparameterization of Low-Rank Adaptation Authors: ['Ayan Sengupta' 'Vaibhav Seth' 'Ashok Kumar Pathak' 'Natraj Raman' 'Sriram Gopalakrishnan' 'Tanmoy Chakraborty'] Large Language Models (LLMs) are highly resource-intensive to fine-tune due to their enormous size. While low-rank adaptation is a prominent…
Authors not listed
Nuclear Magnetic Resonance (NMR) structure determination is an important problem in education, industry, and research. Solving NMR spectra requires expert knowledge, critical thinking, and careful evaluation of multiple features of spectral data. This study explores the capabilities of large language models (LLMs) for…
Authors not listed
Bioprocess engineering has incorporated effective AI applications in recent years that consist of traditional approaches to training models on relevant data to then analyze and predict new and unseen data. The missing component has been the ability to process mixed data from an assortment of dissimilar information…
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…