A Review of Large Language Models and Autonomous Agents in Chemistry

Chemical Science, Год журнала: 2024, Номер unknown

Опубликована: Дек. 9, 2024

Large language models (LLMs) have emerged as powerful tools in chemistry, significantly impacting molecule design, property prediction, and synthesis optimization. This review highlights LLM capabilities these domains their potential to accelerate scientific discovery through automation. We also LLM-based autonomous agents: LLMs with a broader set of interact surrounding environment. These agents perform diverse tasks such paper scraping, interfacing automated laboratories, planning. As are an emerging topic, we extend the scope our beyond chemistry discuss across any domains. covers recent history, current capabilities, design agents, addressing specific challenges, opportunities, future directions chemistry. Key challenges include data quality integration, model interpretability, need for standard benchmarks, while point towards more sophisticated multi-modal enhanced collaboration between experimental methods. Due quick pace this field, repository has been built keep track latest studies: https://github.com/ur-whitelab/LLMs-in-science.

Язык: Английский

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Enhancing Autonomous System Security and Resilience With Generative AI: A Comprehensive Survey

IEEE Access, Год журнала: 2024, Номер 12, С. 109470 - 109493

Опубликована: Янв. 1, 2024

This survey explores the transformative role of Generative Artificial Intelligence (GenAI) in enhancing trustworthiness, reliability, and security autonomous systems such as Unmanned Aerial Vehicles (UAVs), self-driving cars, robotic arms. As edge robots become increasingly integrated into daily life critical infrastructure, complexity connectivity these introduce formidable challenges ensuring security, resilience, safety. GenAI advances from mere data interpretation to autonomously generating new data, proving complex, context-aware environments like robotics. Our delves impact technologies—including Adversarial Networks (GANs), Variational Autoencoders (VAEs), Transformer-based models, Large Language Models (LLMs)—on cybersecurity, decision-making, development resilient architectures. We categorize existing research highlight how technologies address operational innovate predictive maintenance, anomaly detection, adaptive threat response. comprehensive analysis distinguishes this work reviews by mapping out applications, challenges, technological advancements their on creating secure frameworks for systems. discuss significant future directions integrating within evolving landscape cyber-physical threats, underscoring potential make more adaptive, secure, efficient.

Язык: Английский

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A survey on LLM-based multi-agent systems: workflow, infrastructure, and challenges

Vicinagearth., Год журнала: 2024, Номер 1(1)

Опубликована: Окт. 8, 2024

Abstract The pursuit of more intelligent and credible autonomous systems, akin to human society, has been a long-standing endeavor for humans. Leveraging the exceptional reasoning planning capabilities large language models (LLMs), LLM-based agents have proposed achieved remarkable success across wide array tasks. Notably, multi-agent systems (MAS) are considered promising pathway towards realizing general artificial intelligence that is equivalent or surpasses human-level intelligence. In this paper, we present comprehensive survey these studies, offering systematic review MAS. Adhering workflow synthesize structure encompassing five key components: profile, perception, self-action, mutual interaction, evolution. This unified framework encapsulates much previous work in field. Furthermore, illuminate extensive applications MAS two principal areas: problem-solving world simulation. Finally, discuss detail several contemporary challenges provide insights into potential future directions domain.

Язык: Английский

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Let Me Do It For You: Towards LLM Empowered Recommendation via Tool Learning

Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Год журнала: 2024, Номер unknown, С. 1796 - 1806

Опубликована: Июль 10, 2024

Conventional recommender systems (RSs) face challenges in precisely capturing users' fine-grained preferences. Large language models (LLMs) have shown capabilities commonsense reasoning and leveraging external tools that may help address these challenges. However, existing LLM-based RSs suffer from hallucinations, misalignment between the semantic space of items behavior users, or overly simplistic control strategies (e.g., whether to rank directly present results). To bridge gap, we introduce ToolRec, a framework for LLM-empowered recommendations via tool learning uses LLMs as surrogate thereby guiding recommendation process invoking generate list aligns closely with nuanced We formulate aimed at exploring user interests attribute granularity. The factors nuances context LLM then invokes based on user's instructions probes different segments item pool. consider two types attribute-oriented tools: retrieval tools. Through integration LLMs, ToolRec enables conventional become natural interface. Extensive experiments verify effectiveness particularly scenarios are rich content.

Язык: Английский

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Potential Roles of Large Language Models in the Production of Systematic Reviews and Meta-Analyses

Journal of Medical Internet Research, Год журнала: 2024, Номер 26, С. e56780 - e56780

Опубликована: Май 31, 2024

Large language models (LLMs) such as ChatGPT have become widely applied in the field of medical research. In process conducting systematic reviews, similar tools can be used to expedite various steps, including defining clinical questions, performing literature search, document screening, information extraction, and refinement, thereby conserving resources enhancing efficiency. However, when using LLMs, attention should paid transparent reporting, distinguishing between genuine false content, avoiding academic misconduct. this viewpoint, we highlight potential roles LLMs creation reviews meta-analyses, elucidating their advantages, limitations, future research directions, aiming provide insights guidance for authors planning meta-analyses.

Язык: Английский

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