Top Research Papers on LLMs

This survey comprehensively review LLM-empowered recommender systems from various perspectives including pre-training, fine-tuning, and prompting paradigms, and comprehensively discusses the promising future directions in this emerging field.

This paper aims to explore the potential of LLMs in graph machine learning, especially the node classification task, and investigates two possible pipelines: LLMs-as-Enhancers and LLMs-as-Predictors.

This work provides a comprehensive overview of LLMs in the context of language modeling, word embeddings, and deep learning, and examines the application of LLMs in diverse fields including text generation, vision-language models, personalized learning, biomedicine, and code generation.

The ethical guidance debate should be reframed to focus on defining what constitutes acceptable human oversight across the spectrum of applications, which involves considering the diversity of settings, varying potentials for harm, and different acceptable thresholds for performance and certainty in healthcare.

This survey comprehensively review existing research studies in RA-LLMs, covering three primary technical perspectives: architectures, training strategies, and applications, and systematically review mainstream relevant work by their architectures, training strategies, and application areas.

This study highlights the potential benefits of integrating Large Language Models (LLMs) into chemical engineering education. In this study, Chat-GPT, a user-friendly LLM, is used as a problem-solving tool. Chemical engineering education has traditionally focused on fundamental knowledge in the classroom with limited opportunities for hands-on problem-solving. To address this issue, our study proposes an LLMs-assisted problem-solving procedure. This approach promotes critical thinking, enhances problem-solving abilities, and facilitates a deeper understanding of core subjects. Furthermore, inc...

A hackathon that employed large-language models for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications indicates that LLMs will profoundly impact the future of the authors' fields.

Large language models (LLMs) have come closest among all models to date to mastering human language, yet opinions about their linguistic and cognitive capabilities remain split. Here, we evaluate LLMs using a distinction between formal linguistic competence (knowledge of linguistic rules and patterns) and functional linguistic competence (understanding and using language in the world). We ground this distinction in human neuroscience, which has shown that formal and functional competence rely on different neural mechanisms. Although LLMs are surprisingly good at formal competence, their perfor...

A statistical test for detecting the watermark with interpretable p-values is proposed, and an information-theoretic framework for analyzing the sensitivity of the watermarks is derived.

This review explains how large language models (LLMs), such as ChatGPT, are developed and discusses their strengths and limitations in the context of potential clinical applications, as a primer for interested clinicians.

This paper reviews some of the most prominent LLMs, including three popular LLM families (GPT, LLaMA, PaLM), and discusses their characteristics, contributions and limitations, and gives an overview of techniques developed to build, and augment LLMs.

Language is essentially a complex, intricate system of human expressions governed by grammatical rules. It poses a significant challenge to develop capable AI algorithms for comprehending and grasping a language. As a major approach, language modeling has been widely studied for language understanding and generation in the past two decades, evolving from statistical language models to neural language models. Recently, pre-trained language models (PLMs) have been proposed by pre-training Transformer models over large-scale corpora, showing strong capabilities in solving various NLP tasks. Since...

The study found that incorporating ChatGPT influenced the students’ critical, reflective, and creative thinking skills and their dimensions discernibly and provides suggestions for academics, instructional designers, and researchers working in educational technology.

This paper presents a survey of model compression techniques for LLMs, covering methods like quantization, pruning, and knowledge distillation, highlighting recent advancements and offering valuable insights for researchers and practitioners.

A taxonomy that categorizes these models into two major paradigms, respectively Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec), with the latter being systematically sorted out for the first time.

Four experts in artificial intelligence ethics and policy discuss potential risks and call for careful consideration and responsible usage to ensure that good scientific practices and trust in science are not compromised.

Interacting with a contemporary LLM-based conversational agent can create an illusion of being in the presence of a thinking creature, yet such systems are fundamentally not like us.

Two important cases of dialogue-agent behaviour are addressed this way, namely, (apparent) deception and (apparent) self-awareness, namely, (apparent) deception and (apparent) self-awareness.

This Viewpoint seeks to stimulate broader discourse on the potential of large language models in ophthalmology and to galvanise both clinicians and researchers into tackling the prevailing challenges and optimising the benefits ofLarge language models while curtailing the associated risks.

It is concluded that while it is somewhat unlikely that current large language models are conscious, the possibility that successors to large language models may be conscious in the not-too-distant future should be taken seriously.