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.

A synthesis of current research trends is provided and potential directions for future research to address bias and hallucination in LLMs are suggested, considering the ongoing challenges in this field.

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.

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.

The authors design the system architecture and prompts to create personas, ask questions, and obtain responses from synthetic respondents and conclude that LLMs serve as valuable collaborators in the insight generation process.

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.

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.

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...

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.

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 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.

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.

A range of multimodal products are introduced, focusing on the efforts of major technology companies, and a compilation of the latest algorithms and commonly used datasets are presented, providing researchers with valuable resources for experimentation and evaluation.

This paper discusses an unpredictable phenomenon that is referred to as emergent abilities of large language models, an ability to be emergent if it is not present in smaller models but is present in larger models.

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.

Information overload is a major obstacle to scientific progress. The explosive growth in scientific literature and data has made it ever harder to discover useful insights in a large mass of information. Today scientific knowledge is accessed through search engines, but they are unable to organize scientific knowledge alone. In this paper we introduce Galactica: a large language model that can store, combine and reason about scientific knowledge. We train on a large scientific corpus of papers, reference material, knowledge bases and many other sources. We outperform existing models on a range...