Large Language Models: New Opportunities for Access to Science

• URL: http://arxiv.org/abs/2501.07250v1
• Date: Mon, 13 Jan 2025 11:58:27 GMT
• Title: Large Language Models: New Opportunities for Access to Science
• Abstract summary: The uptake of Retrieval Augmented Generation-enhanced chat applications in the construction of the open science environment of the KM3NeT neutrino detectors serves as a focus point to explore and exemplify prospects for the wider application of Large Language Models for our science.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The adaptation of Large Language Models like ChatGPT for information retrieval from scientific data, software and publications is offering new opportunities to simplify access to and understanding of science for persons from all levels of expertise. They can become tools to both enhance the usability of the open science environment we are building as well as help to provide systematic insight to a long-built corpus of scientific publications. The uptake of Retrieval Augmented Generation-enhanced chat applications in the construction of the open science environment of the KM3NeT neutrino detectors serves as a focus point to explore and exemplify prospects for the wider application of Large Language Models for our science.

Related papers

• From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry [109.17159174400912]
  Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowledge.<n>This paper analyzes a broad set of community-developed LLM applications in an effort to identify emerging patterns in how these systems can be used across the scientific research lifecycle.
  arXiv  Detail & Related papers  (2026-05-04T22:48:45Z)
• SciEvalKit: An Open-source Evaluation Toolkit for Scientific General Intelligence [99.30934038146965]
  SciEvalKit focuses on the core competencies of scientific intelligence.<n>It supports six major scientific domains, spanning from physics and chemistry to astronomy and materials science.<n>The toolkit is open-sourced and actively maintained to foster community-driven development and progress in AI4Science.
  arXiv  Detail & Related papers  (2025-12-26T17:36:02Z)
• The Empowerment of Science of Science by Large Language Models: New Tools and Methods [7.392576669260369]
  Large language models (LLMs) have exhibited exceptional capabilities in natural language understanding and generation, image recognition, and multimodal tasks.<n>This manuscript conducts a comprehensive review of the core technologies that support LLMs from a user standpoint, including prompt engineering, knowledge-enhanced retrieval augmented generation, fine tuning, pretraining, and tool learning.
  arXiv  Detail & Related papers  (2025-11-19T11:57:22Z)
• Autonomous Agents for Scientific Discovery: Orchestrating Scientists, Language, Code, and Physics [82.55776608452017]
  Large language models (LLMs) provide a flexible and versatile framework that orchestrates interactions with human scientists, natural language, computer language and code, and physics.<n>This paper presents our view and vision of LLM-based scientific agents and their growing role in transforming the scientific discovery lifecycle.<n>We identify open research challenges and outline promising directions for building more robust, generalizable, and adaptive scientific agents.
  arXiv  Detail & Related papers  (2025-10-10T22:26:26Z)
• A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers [251.23085679210206]
  Scientific Large Language Models (Sci-LLMs) are transforming how knowledge is represented, integrated, and applied in scientific research.<n>This survey reframes the development of Sci-LLMs as a co-evolution between models and their underlying data substrate.<n>We formulate a unified taxonomy of scientific data and a hierarchical model of scientific knowledge.
  arXiv  Detail & Related papers  (2025-08-28T18:30:52Z)
• AstroVisBench: A Code Benchmark for Scientific Computing and Visualization in Astronomy [59.32718342798908]
  We introduce AstroVisBench, the first benchmark for both scientific computing and visualization in the astronomy domain.<n>We present an evaluation of state-of-the-art language models, showing a significant gap in their ability to engage in astronomy research as useful assistants.
  arXiv  Detail & Related papers  (2025-05-26T21:49:18Z)
• How Scientists Use Large Language Models to Program [0.0]
  We investigate the characteristics of scientists who are early-adopters of code generating models. We see that scientists often use code generating models as an information retrieval tool for navigating unfamiliar programming languages and libraries.
  arXiv  Detail & Related papers  (2025-02-24T17:23:12Z)
• ByteScience: Bridging Unstructured Scientific Literature and Structured Data with Auto Fine-tuned Large Language Model in Token Granularity [13.978222668670192]
  ByteScience is a non-profit cloud-based auto fine-tuned Large Language Model (LLM) platform.<n>It is designed to extract structured scientific data and synthesize new scientific knowledge from vast scientific corpora.<n>The platform achieves remarkable accuracy with only a small amount of well-annotated articles.
  arXiv  Detail & Related papers  (2024-11-18T19:36:26Z)
• A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery [68.48094108571432]
  Large language models (LLMs) have revolutionized the way text and other modalities of data are handled. We aim to provide a more holistic view of the research landscape by unveiling cross-field and cross-modal connections between scientific LLMs.
  arXiv  Detail & Related papers  (2024-06-16T08:03:24Z)
• LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery [141.39722070734737]
  We propose to enhance the knowledge-driven, abstract reasoning abilities of Large Language Models with the computational strength of simulations. We introduce Scientific Generative Agent (SGA), a bilevel optimization framework. We conduct experiments to demonstrate our framework's efficacy in law discovery and molecular design.
  arXiv  Detail & Related papers  (2024-05-16T03:04:10Z)
• Scientific Large Language Models: A Survey on Biological & Chemical Domains [47.97810890521825]
  Large Language Models (LLMs) have emerged as a transformative power in enhancing natural language comprehension. The application of LLMs extends beyond conventional linguistic boundaries, encompassing specialized linguistic systems developed within various scientific disciplines. As a burgeoning area in the community of AI for Science, scientific LLMs warrant comprehensive exploration.
  arXiv  Detail & Related papers  (2024-01-26T05:33:34Z)
• Understanding Practices around Computational News Discovery Tools in the Domain of Science Journalism [3.660182910533372]
  We explore computational methods to aid these journalists' news discovery in terms of time-efficiency and agency. We prototyped three computational information subsidies into an interactive tool that we used as a probe to better understand how such a tool may offer utility. Our findings contribute a richer view of the sociotechnical system around computational news discovery tools, and suggest ways to improve such tools to better support the practices of science journalists.
  arXiv  Detail & Related papers  (2023-11-12T14:47:50Z)
• Large Language Models for Scientific Synthesis, Inference and Explanation [56.41963802804953]
  We show how large language models can perform scientific synthesis, inference, and explanation. We show that the large language model can augment this "knowledge" by synthesizing from the scientific literature. This approach has the further advantage that the large language model can explain the machine learning system's predictions.
  arXiv  Detail & Related papers  (2023-10-12T02:17:59Z)
• Modeling Information Change in Science Communication with Semantically Matched Paraphrases [50.67030449927206]
  SPICED is the first paraphrase dataset of scientific findings annotated for degree of information change. SPICED contains 6,000 scientific finding pairs extracted from news stories, social media discussions, and full texts of original papers. Models trained on SPICED improve downstream performance on evidence retrieval for fact checking of real-world scientific claims.
  arXiv  Detail & Related papers  (2022-10-24T07:44:38Z)
• KnowledgeShovel: An AI-in-the-Loop Document Annotation System for Scientific Knowledge Base Construction [46.56643271476249]
  KnowledgeShovel is an Al-in-the-Loop document annotation system for researchers to construct scientific knowledge bases. The design of KnowledgeShovel introduces a multi-step multi-modalAI collaboration pipeline to improve data accuracy while reducing the human burden. A follow-up user evaluation with 7 geoscience researchers shows that KnowledgeShovel can enable efficient construction of scientific knowledge bases with satisfactory accuracy.
  arXiv  Detail & Related papers  (2022-10-06T11:38:18Z)
• Semantic and Relational Spaces in Science of Science: Deep Learning Models for Article Vectorisation [4.178929174617172]
  We focus on document-level embeddings based on the semantic and relational aspects of articles, using Natural Language Processing (NLP) and Graph Neural Networks (GNNs) Our results show that using NLP we can encode a semantic space of articles, while with GNN we are able to build a relational space where the social practices of a research community are also encoded.
  arXiv  Detail & Related papers  (2020-11-05T14:57:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.