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.

Time-LLM is a reprogramming framework to repurpose LLMs for general time series forecasting with the backbone language models kept intact and is demonstrated to be a powerful time series learner that outperforms state-of-the-art, specialized forecasting models.

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.

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...

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.

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.

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...

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.

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.

This paper presents the basic formulation of the MLLM and delineates its related concepts, including architecture, training strategy and data, as well as evaluation, and introduces research topics about how MLLMs can be extended to support more granularity, modalities, languages and scenarios.

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.

Large language models (LLMs), such as OpenAI's GPT-4, Google's Bard or Meta's LLaMa, have created unprecedented opportunities for analysing and generating language data on a massive scale. Because language data have a central role in all areas of psychology, this new technology has the potential to transform the field. In this Perspective, we review the foundations of LLMs. We then explain how the way that LLMs are constructed enables them to effectively generate human-like linguistic output without the ability to think or feel like a human. We argue that although LLMs have the potential to ad...

A decision framework is proposed to guide financial professionals in selecting the appropriate LLM solution based on their use case constraints around data, compute, and performance needs and provides a pathway from lightweight experimentation to heavy investment in customized LLMs.

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...

A taxonomy of explainability techniques is introduced and a structured overview of methods for explaining Transformer-based language models is provided, based on the training paradigms of LLMs: traditional fine-tuning-based paradigm and prompting-based paradigm.

Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks and beyond. This success of LLMs has led to a large influx of research contributions in this direction. These works encompass diverse topics such as architectural innovations, better training strategies, context length improvements, fine-tuning, multi-modal LLMs, robotics, datasets, benchmarking, efficiency, and more. With the rapid development of techniques and regular breakthroughs in LLM research, it has become considerably challenging to perceive the bigger picture of the ad...

This paper aims to establish a systematic set of open problems and application successes so that ML researchers can comprehend the field's current state more quickly and become productive.

The use of NLP in the realm of financial technology is broad and complex, with applications ranging from sentiment analysis and named entity recognition to question answering. Large Language Models (LLMs) have been shown to be effective on a variety of tasks; however, no LLM specialized for the financial domain has been reported in literature. In this work, we present BloombergGPT, a 50 billion parameter language model that is trained on a wide range of financial data. We construct a 363 billion token dataset based on Bloomberg's extensive data sources, perhaps the largest domain-specific data...

This paper presents a comprehensive review of these evaluation methods for LLMs, focusing on three key dimensions: what to evaluate, where to evaluate, and how to evaluate, and offers invaluable insights to researchers in the realm of LLMs evaluation.

Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes increasingly critical, not only at the task level, but also at the society level for better understanding of their potential risks. Over the past years, significant efforts have been made to examine LLMs from various perspectives. This paper presents a comprehensive review of these evaluation methods for LLMs, focusing on three key dimensions:...

This review article is intended to provide a quick, comprehensive reference for the researchers and practitioners to draw insights from extensive, informative summaries of the existing works to advance the LLM research.

TitanFuzz is demonstrated that modern titanic LLMs can be leveraged to directly perform both generation-based and mutation-based fuzzing studied for decades, while being fully automated, generalizable, and applicable to domains challenging for traditional approaches.

It is suggested that the most recent generation of Large Language Models (LLMs) might finally provide the computational tools to determine empirically how much of the human language ability can be acquired from linguistic experience.

This paper investigates LLMs’ behavior with respect to gender stereotypes, a known issue for prior models, and suggests that LLMs must be carefully tested to ensure that they treat minoritized individuals and communities equitably.

This work built Wordcraft, a text editor in which users collaborate with a generative language model to write a story, and shows that large language models enable novel co-writing experiences.

Opposition perspectives for and against the use of~LLMs for automatic relevance judgments are provided, informed by the analyses of the literature, the preliminary experimental evidence, and the experience as IR~researchers.

A large clinical language model—GatorTron—is developed from scratch and systematically evaluate it on five clinical NLP tasks including clinical concept extraction, medical relation extraction, semantic textual similarity, natural language inference (NLI), and medical question answering (MQA).

Coscientist showcases its potential for accelerating research across six diverse tasks, including the successful reaction optimization of palladium-catalysed cross-couplings, while exhibiting advanced capabilities for (semi-)autonomous experimental design and execution.