Top Research Papers on LLM Models

A systematic review of the evaluation parameters and methodologies applied to LLMs in clinical medicine highlights certain limitations and biases across the included studies, emphasizing the need for careful interpretation and robust evaluation frameworks.

Transparent reporting of a multivariable model for individual prognosis or diagnosis–large language model TRIPOD-LLM is a checklist of items considered essential for good reporting of studies that are developing or evaluating an LLM for use in healthcare settings, a ‘living guideline’ that emphasizes transparency, human oversight and task-specific performance reporting.

This work investigates how LLMs positively impact security and privacy, potential risks and threats associated with their use, and inherent vulnerabilities within LLMs, and identifies areas that require further research efforts.

Zechun Liu, Barlas Oguz, Changsheng Zhao, Ernie Chang, Pierre Stock, Yashar Mehdad, Yangyang Shi, Raghuraman Krishnamoorthi, Vikas Chandra. Findings of the Association for Computational Linguistics: ACL 2024. 2024.

This work proposes a novel method, LLM-Planner, that harnesses the power of large language models to do few-shot planning for embodied agents and proposes a simple but effective way to enhance LLMs with physical grounding to generate and update plans that are grounded in the current environment.

This work presents LLM fundamentals, including model architecture, pre-training, fine-tuning, inference and utilization, model evaluation, and telecom deployment, and introduces LLM-enabled key techniques and telecom applications in terms of generation, classification, optimization, and prediction problems.

The success of large language models (LLMs), like GPT-4 and ChatGPT, has led to the development of numerous cost-effective and accessible alternatives that are created by finetuning open-access LLMs with task-specific data (e.g., ChatDoctor) or instruction data (e.g., Alpaca). Among the various fine-tuning methods, adapter-based parameter-efficient fine-tuning (PEFT) is undoubtedly one of the most attractive topics, as it only requires fine-tuning a few external parameters instead of the entire LLMs while achieving comparable or even better performance. To enable further research on PEFT metho...

The results show Libro has the potential to significantly enhance developer efficiency by automatically generating tests from bug reports, a framework that uses Large Language Models (LLMs), which have been shown to be capable of performing code-related tasks.

The crux of the study centers around LangChain, designed to expedite the development of bespoke AI applications using LLMs, and provides an examination of its core features, including its components and chains, acting as modular abstractions and customizable, use-case-specific pipelines, respectively.

SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models (LLMs), harnesses the power of LLMs to convert high-level task instructions provided as input into a multi-robot task plan by executing a series of stages.

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.

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...