research papers on artificial intelligence free download

The Journal of Artificial Intelligence Research (JAIR) is dedicated to the rapid dissemination of important research results to the global artificial intelligence (AI) community. The journal’s scope encompasses all areas of AI, including agents and multi-agent systems, automated reasoning, constraint processing and search, knowledge representation, machine learning, natural language, planning and scheduling, robotics and vision, and uncertainty in AI.

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Vol. 80 (2024)

Published: 2024-05-10

Probabilities of the Third Type: Statistical Relational Learning and Reasoning with Relative Frequencies

Mixed fair division: a survey, the complexity of subelection isomorphism problems, towards trustworthy ai-enabled decision support systems: validation of the multisource ai scorecard table (mast), computational argumentation-based chatbots: a survey, from single-objective to bi-objective maximum satisfiability solving, a hybrid intelligence method for argument mining, tackling cooperative incompatibility for zero-shot human-ai coordination, symbolic task inference in deep reinforcement learning, on the convergence of swap dynamics to pareto-optimal matchings, does clip know my face, axiomatization of non-recursive aggregates in first-order answer set programming, unifying sat-based approaches to maximum satisfiability solving, viewpoint: hybrid intelligence supports application development for diabetes lifestyle management, sat-based decision tree learning for large data sets, individual fairness, base rate tracking and the lipschitz condition, simulating counterfactuals, counting complexity for reasoning in abstract argumentation, robust average-reward reinforcement learning, using constraint propagation to bound linear programs, the toad system for totally ordered htn planning, methods for recovering conditional independence graphs: a survey, best of both worlds: agents with entitlements, computing unsatisfiable cores for ltlf specifications, general policies, subgoal structure, and planning width, mitigating value hallucination in dyna-style planning via multistep predecessor models, exploiting contextual target attributes for target sentiment classification, similarity-based adaptation for task-aware and task-free continual learning, scalable primal heuristics using graph neural networks for combinatorial optimization, on the trade-off between redundancy and cohesiveness in extractive summarization, understanding sample generation strategies for learning heuristic functions in classical planning, block domain knowledge-driven learning of chain graphs structure, expressing and exploiting subgoal structure in classical planning using sketches, effectiveness of tree-based ensembles for anomaly discovery: insights, batch and streaming active learning, experimental design of extractive question-answering systems: influence of error scores and answer length, estimating agent skill in continuous action domains, computing pareto-optimal and almost envy-free allocations of indivisible goods.

Journal of Artificial Intelligence Research

• AI Access Foundation
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The Journal of Artificial Intelligence Research ( www.jair.org ) covers all areas of artificial intelligence, publishing refereed research articles, survey articles, and technical notes. JAIR was established in 1993 as one of the very first open access scientific journals on the Web. Since it began publication in 1993, JAIR has had a major impact on the field, and has been continuously ranked as one of the top journals covering AI.

To submit articles and see journal details visit www.jair.org

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• Volume 80 Jul 2024 ISSN: 1076-9757 View Table of Contents

Understanding Sample Generation Strategies for Learning Heuristic Functions in Classical Planning

• Rafael V. Bettker ,

a:1:{s:5:"en_US";s:39:"Federal University of Rio Grande do Sul";}

• Marcus Ritt

The TOAD System for Totally Ordered HTN Planning

Saarland University, Saarland Informatics Campus, Saarbrücken, Germany

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Join the community, search results, facechain: a playground for human-centric artificial intelligence generated content.

1 code implementation • 28 Aug 2023

In this paper, we present FaceChain, a personalized portrait generation framework that combines a series of customized image-generation model and a rich set of face-related perceptual understanding models (\eg, face detection, deep face embedding extraction, and facial attribute recognition), to tackle aforementioned challenges and to generate truthful personalized portraits, with only a handful of portrait images as input.

Explaining reaction coordinates of alanine dipeptide isomerization obtained from deep neural networks using Explainable Artificial Intelligence (XAI)

1 code implementation • 15 Feb 2022

A method for obtaining appropriate reaction coordinates is required to identify transition states distinguishing product and reactant in complex molecular systems.

Chemical Physics Soft Condensed Matter

Data-centric Artificial Intelligence: A Survey

10 code implementations • 17 Mar 2023

Artificial Intelligence (AI) is making a profound impact in almost every domain.

Sparks of Artificial General Intelligence: Early experiments with GPT-4

2 code implementations • 22 Mar 2023

We contend that (this early version of) GPT-4 is part of a new cohort of LLMs (along with ChatGPT and Google's PaLM for example) that exhibit more general intelligence than previous AI models.

Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence

2 code implementations • 12 Feb 2020

Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline.

MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence

3 code implementations • 2 Dec 2017

Unlike previous research platforms on single or multi-agent reinforcement learning, MAgent focuses on supporting the tasks and the applications that require hundreds to millions of agents.

DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker

1 code implementation • 6 Jan 2017

Poker is the quintessential game of imperfect information, and a longstanding challenge problem in artificial intelligence.

A System for Accessible Artificial Intelligence

2 code implementations • 1 May 2017

While artificial intelligence (AI) has become widespread, many commercial AI systems are not yet accessible to individual researchers nor the general public due to the deep knowledge of the systems required to use them.

Adversarial attacks and defenses in explainable artificial intelligence: A survey

1 code implementation • 6 Jun 2023

Explainable artificial intelligence (XAI) methods are portrayed as a remedy for debugging and trusting statistical and deep learning models, as well as interpreting their predictions.

The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence

1 code implementation • 22 Sep 2021

We present the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) Multifield Dataset, CMD, a collection of hundreds of thousands of 2D maps and 3D grids containing many different properties of cosmic gas, dark matter, and stars from 2, 000 distinct simulated universes at several cosmic times.

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70 recent research papers in Deep Learning – Free Download

Artificial intelligence (AI) is a thriving field in this century with many practical applications. We look to AI and machine learning tools to automate routine labor, understand speech or images, make diagnoses in medicine, and support basic scientific research.

Deep Learning is a relatively new area, introduced to move machine learning closer to one of its original goals: Artificial Intelligence . It is an approach to AI or a type of machine learning that allows the computers to build complex concepts from simpler concepts and represent the world as a nested hierarchy of concepts.

Deep Learning introduces multiple levels of representation in which more abstract representations are computed with simpler representations. This helps us make sense of complex datasets such as images, videos, sound, etc. In just the past few years, deep learning has taken the world by surprise, driving rapid progress in computer vision, natural language processing (NLP), speech recognition, reinforcement learning, etc.

With these advancements, we can now build cars that drive themselves with more autonomy than ever before, smart reply systems that automatically draft the most mundane emails, and software agents that dominate humans. They already have an ever-widening impact on our lives, playing a big role in technology and sciences — from biology to astrophysics.

In this post, we list the top 70 research papers and projects in deep learning, published recently. Feel free to download. Share your own research papers with us to be added to this list.

• Person re-identification by deep learning multi-scale representations
• Classification of Diabetic Retinopathy Images by Using Deep Learning Models
• Lower jawbone data generation for deep learning tools under MeVisLab
• Theory of deep learning III: the non-overfitting puzzle
• Weblogo-2m: Scalable logo detection by deep learning from the web
• Supplementary Materials for DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks
• Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm
• Discrete Deep Learning for Fast Content-Aware Recommendation
• Travel Behavior Classification: An Approach with Social Network and Deep Learning 2
• Machine Translation Using Deep Learning : A Survey
• Deep Learning for Joint Source-Channel Coding of Text
• DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks
• Learning To Share: Simultaneous Parameter Tying And Sparsification In Deep Learning
• Deep Inductive Network Representation Learning
• Exploration and Tradeoffs of Different Kernels in FPGA Deep Learning Applications
• A Deep Model with Local Surrogate Loss for General Cost-sensitive Multi-label Learning
• LSH Softmax: Sub-Linear Learning and Inference of the Softmax Layer in Deep Architectures
• Perception-Action- Learning System for Mobile Social-Service Robots using Deep Learning
• Geometry and Uncertainty in Deep Learning for Computer Vision
• Identifying RNA-binding proteins using multi-label deep learning
• A Deep Reinforcement Learning Network for Traffic Light Cycle Control
• Deep Learning for Cost-Optimal Planning: Task-Dependent Planner Selection
• Towards the quantification of uncertainty for deep learning based rainfall-runoff models
• DeepSqueak: a deep learning -based system for detection and analysis of ultrasonic vocalizations
• Investigating the Feasibility of Finger Identification on Capacitive Touchscreens using Deep Learning
• WiDeep: WiFi-based accurate and robust indoor localization system using deep learning
• The unreasonable effectiveness of deep learning in artificial intelligence
• Is Deep Learning a Game Changer for Marketing Analytics
• Robust Deep Learning as Optimal Control: Insights and Convergence Guarantees
• Deep Learning in Ultrasound Imaging
• Improving Lives of Indebted Farmers Using Deep Learning : Predicting Agricultural Produce Prices Using Convolutional Neural Networks
• Automating Cyberdeception Evaluation with Deep Learning
• When Deep Learning meets Web Measurements to infer Network Performance
• Comparison of semi-automatic and deep learning -based automatic methods for liver segmentation in living liver transplant donors
• Biomedical Imaging and Analysis In the Age of Sparsity, Big Data, and Deep Learning
• Deep Learning on the 2-Dimensional Ising Model to Extract the Crossover Region
• A Deep Learning Approach to Understanding Cloud Service Level Agreements
• Using Deep Learning to Automate Feature Modeling in Learning by Observation: A Preliminary Study
• Deep Structured Learning for Facial Expression Intensity Estimation
• Deep Learning for abnormality detection in Chest X-Ray images
• The Design and Evolution of Deep Learning Workloads
• A Systematic Literature Review on Features of Deep Learning in Big Data Analytics.
• Deep learning on CManifolds
• Plant Leaf Disease Detection using Deep Learning and Convolutional Neural Network
• Smart Library: Identifying Books on Library Shelves using Supervised Deep Learning for Scene Text Reading
• A new semantic attribute deep learning with a linguistic attribute hierarchy for spam detection
• Plant identification based on noisy web data: the amazing performance of deep learning (LifeCLEF 2017)
• Deep Learning: Shaking the Founda ons
• Fall Risk Reduction for the Elderly Using Mobile Robots Based on the Deep Reinforcement Learning
• Object Classification in Images of Neoclassical Artifacts Using Deep Learning
• Simulation based optimal control via deep learning
• Deep learning with geodesic moments for 3D shape classification
• Description of Images Related To Haze Crisis in Indonesia Using Deep Learning
• Application of deep learning neural network for classification of TB lung CT images based on patches
• Forecasting Real Time Series Data using Deep Belief Net and Reinforcement Learning
• Scaling Up Deep Learning on Clusters
• Unsupervised multi-manifold clustering by learning deep representation
• An Extensive Survey on Deep Learning Applications
• A Quick Review of Deep Learning in Facial Expression
• Query by Singing/Humming System Based on Deep Learning
• Deep Learning for Predictions in Emerging Currency Markets.
• Deep Learning Approach for Secondary Structure Protein Prediction based on First Level Features Extraction using a Latent CNN Structure
• Deep Learning for Intelligent Transportation
• Deep reinforcement learning for dynamic multichannel access
• Automated Feature Selection and Churn Prediction using Deep Learning Models
• Deep Learning for Natural Language Processing
• Development and Applications of Deep Learning Structures for Point Cloud Data
• International Workshop on Deep Learning and Music
• Deep Learning Binary Neural Network on an FPGA
• Wildcat: Weakly supervised learning of deep convnets for image classification, pointwise localization and segmentation

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8 Open Access Resources For AI & ML Research Papers

• by Anirudh VK

Whether one is a beginner in the AI field and wishes to gain more in-depth knowledge, or an expert looking to widen his field of knowledge, here are 8 ML, DL and AI Open Access resources for research papers.

1. Neural Information Processing Systems Foundation

The NIPS Foundation blog houses research papers regarding the progress of neural networks and systems regarding them. What’s shocking is that they have been doing so since 1987, offering users the unique opportunity to look at how the fundamentals of neural networks emerged. With hundreds upon hundreds of papers available at the site, it is a great resource for learning online.

2. Academia.edu

Academia.edu functions as a platform for academicians to share research papers regarding their general field. The website uses analytics to measure impact, with over 22 million papers added to the platform as of now. The website also has hundreds of machine learning papers available for viewing or download after signing up with an email ID.

Dedicated communities of ML enthusiasts have created multiple lists of the must-reads in the machine learning and deep learning fields. These include not only curated lists of some of the influential papers published over the past few years, but also some hidden gems and a vast amount of knowledge. Some examples include terryum ‘s “Awesome – Most Cited Deep Learning Papers”, and floodsung ‘s “Deep Learning Papers Reading Roadmap” collections.

4. arXiv.org

arXiv.org is not the and top-voted great place to read research papers on a wide variety of topics, but also functions as a repository of ML and DL papers. The collection of research papers on the platform are over 1.5 million, with over 36,000 papers for machine learning alone. It is a great place for those looking to get started on reading about ML and DL. The platform will also expose individuals to a wide range of applications of AI technology due to its vast range of well-written papers.

This world famous entity was founded with a mission to advance AI research and safety and make it more human-centric. It recently turned for profit and is billed as a go-to platform for the latest research papers regarding AI development, especially in the field of Reinforcement Learning. They have contributed 16 papers to the ML community, along with open-sourcing the models and training data used.

6. Computer Vision Foundation

The Computer Vision Foundation has been a leading voice in the computer vision space, with multiple conferences starting from 2012 onwards. Starting from 2013, the research from these conventions have been archived in an open access format so as to allow further research to take place in the field of computer vision. Moreover, it also has a stance to open up the field so that individuals from across the globe can access information on it.

7. Journal of Machine Learning Research

JMLR website provides the papers published in the journal from 2000 onwards freely online, bringing high quality ML research papers to the public. Starting from October 2000, 20 volumes have been published on the website, with each containing anywhere from 50-100 research papers on Machine Learning

8. Association for Computational Linguistics

This is an association that researches the applications and science of computational linguistics , along with publishing a journal by the name of “Computational Linguistics”. The journal is also Open Access, bringing more than 30 years of research in the NLP field to the web for everyone to learn. Each year has 4 issues, offering a lot of information regarding the rise of NLP over the years.

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Uncover the latest and most impactful research in Artificial Intelligence. Explore pioneering discoveries, insightful ideas and new methods from leading researchers in the field.

Latest research

Exploring the mechanism of path-creating strategy for latecomers: a combined approach of econometrics and causal machine learning.

• Yuanyang Teng

Subject-specific atlas for automatic brain tissue segmentation of neonatal magnetic resonance images

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Impacts of generative artificial intelligence in higher education: research trends and students’ perceptions.

1. Introduction

2. materials and methods.

• “Generative Artificial Intelligence” or “Generative AI” or “Gen AI”, AND;
• “Higher Education” or “University” or “College” or “Post-secondary”, AND;
• “Impact” or “Effect” or “Influence”.
• Q1— Does GenAI have more positive or negative effects on higher education? Options (to choose one): 1. It has more negative effects than positives; 2. It has more positive effects than negative; 3. There is a balance between positive and negative effects; 4. Don’t know.
• Q2— Identify the main positive effect of Gen AI in an academic context . Open-ended question.
• Q3— Identify the main negative effect of Gen AI in an academic context . Open-ended question.

3.1. Impacts of Gen AI in HE: Research Trends

3.1.1. he with gen ai, the key role that pedagogy must play, new ways to enhance the design and implementation of teaching and learning activities.

• Firstly, prompting in teaching should be prioritized as it plays a crucial role in developing students’ abilities. By providing appropriate prompts, educators can effectively guide students toward achieving their learning objectives.
• Secondly, configuring reverse prompting within the capabilities of Gen AI chatbots can greatly assist students in monitoring their learning progress. This feature empowers students to take ownership of their education and fosters a sense of responsibility.
• Furthermore, it is essential to embed digital literacy in all teaching and learning activities that aim to leverage the potential of the new Gen AI assistants. By equipping students with the necessary skills to navigate and critically evaluate digital resources, educators can ensure that they are prepared for the digital age.

The Student’s Role in the Learning Experience

The key teacher’s role in the teaching and learning experience, 3.1.2. assessment in gen ai/chatgpt times, the need for new assessment procedures, 3.1.3. new challenges to academic integrity policies, new meanings and frontiers of misconduct, personal data usurpation and cheating, 3.2. students’ perceptions about the impacts of gen ai in he.

• “It harms the learning process”: ▪ “What is generated by Gen AI has errors”; ▪ “Generates dependence and encourages laziness”; ▪ “Decreases active effort and involvement in the learning/critical thinking process”.

4. Discussion

• Training: providing training for both students and teachers on effectively using and integrating Gen AI technologies into teaching and learning practices.
• Ethical use and risk management: developing policies and guidelines for ethical use and risk management associated with Gen AI technologies.
• Incorporating AI without replacing humans: incorporating AI technologies as supplementary tools to assist teachers and students rather than replacements for human interaction.
• Continuously enhancing holistic competencies: encouraging the use of AI technologies to enhance specific skills, such as digital competence and time management, while ensuring that students continue to develop vital transferable skills.
• Fostering a transparent AI environment: promoting an environment in which students and teachers can openly discuss the benefits and concerns associated with using AI technologies.
• Data privacy and security: ensuring data privacy and security using AI technologies.
• The dynamics of technological support to align with the most suitable Gen AI resources;
• The training policy to ensure that teachers, students, and academic staff are properly trained to utilize the potential of Gen AI and its tools;
• Security and data protection policies;
• Quality and ethical action policies.

5. Conclusions

• Database constraints: the analysis is based on existing publications in SCOPUS and the Web of Science, potentially omitting relevant research from other sources.
• Inclusion criteria: due to the early stage of scientific production on this topic, all publications were included in the analysis, rather than focusing solely on articles from highly indexed journals and/or with a high number of citations as recommended by bibliometric and systematic review best practices.
• Dynamic landscape: the rate of publications on Gen AI has been rapidly increasing and diversifying in 2024, highlighting the need for ongoing analysis to track trends and changes in scientific thinking.

Author Contributions

Institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• Akakpo, Martin Gameli. 2023. Skilled for the Future: Information Literacy for AI Use by University Students in Africa and the Role of Librarians. Internet Reference Services Quarterly 28: 19–26. [ Google Scholar ] [ CrossRef ]
• AlAfnan, Mohammad Awad, Samira Dishari, Marina Jovic, and Koba Lomidze. 2023. ChatGPT as an Educational Tool: Opportunities, Challenges, and Recommendations for Communication, Business Writing, and Composition Courses. Journal of Artificial Intelligence and Technology 3: 60–68. [ Google Scholar ] [ CrossRef ]
• Almaraz-López, Cristina, Fernando Almaraz-Menéndez, and Carmen López-Esteban. 2023. Comparative Study of the Attitudes and Perceptions of University Students in Business Administration and Management and in Education toward Artificial Intelligence. Education Sciences 13: 609. [ Google Scholar ] [ CrossRef ]
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Saúde, S.; Barros, J.P.; Almeida, I. Impacts of Generative Artificial Intelligence in Higher Education: Research Trends and Students’ Perceptions. Soc. Sci. 2024 , 13 , 410. https://doi.org/10.3390/socsci13080410

Saúde S, Barros JP, Almeida I. Impacts of Generative Artificial Intelligence in Higher Education: Research Trends and Students’ Perceptions. Social Sciences . 2024; 13(8):410. https://doi.org/10.3390/socsci13080410

Saúde, Sandra, João Paulo Barros, and Inês Almeida. 2024. "Impacts of Generative Artificial Intelligence in Higher Education: Research Trends and Students’ Perceptions" Social Sciences 13, no. 8: 410. https://doi.org/10.3390/socsci13080410

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• 14 August 2024

Has your paper been used to train an AI model? Almost certainly

• Elizabeth Gibney

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Academic publisher Wiley has sold access to its research papers to firms developing large language models. Credit: Timon Schneider/Alamy

Academic publishers are selling access to research papers to technology firms to train artificial-intelligence (AI) models. Some researchers have reacted with dismay at such deals happening without the consultation of authors. The trend is raising questions about the use of published and sometimes copyrighted work to train the exploding number of AI chatbots in development.

Experts say that, if a research paper hasn’t yet been used to train a large language model (LLM), it probably will be soon. Researchers are exploring technical ways for authors to spot if their content being used.

AI models fed AI-generated data quickly spew nonsense

Last month, it emerged that the UK academic publisher Taylor & Francis, had signed a US$10-million deal with Microsoft, allowing the US technology company to access the publisher’s data to improve its AI systems. And in June, an investor update showed that US publisher Wiley had earned $23 million from allowing an unnamed company to train generative-AI models on its content.

Anything that is available to read online — whether in an open-access repository or not — is “pretty likely” to have been fed into an LLM already, says Lucy Lu Wang, an AI researcher at the University of Washington in Seattle. “And if a paper has already been used as training data in a model, there’s no way to remove that paper after the model has been trained,” she adds.

Massive data sets

LLMs train on huge volumes of data, frequently scraped from the Internet. They derive patterns between the often billions of snippets of language in the training data, known as tokens, that allow them to generate text with uncanny fluency.

Generative-AI models rely on absorbing patterns from these swathes of data to output text, images or computer code. Academic papers are valuable for LLM builders owing to their length and “high information density”, says Stefan Baack, who analyses AI training data sets at the Mozilla Foundation, a global non-profit organization in San Francisco, California that aims to keep the Internet open for all to access.

How does ChatGPT ‘think’? Psychology and neuroscience crack open AI large language models

Training models on a large body of scientific information also give them a much better ability to reason about scientific topics, says Wang, who co-created S2ORC, a data set based on 81.1 million academic papers. The data set was originally developed for text mining — applying analytical techniques to find patterns in data — but has since been used to train LLMs.

The trend of buying high-quality data sets is growing. This year, the Financial Times has offered its content to ChatGPT developer OpenAI in a lucrative deal, as has the online forum Reddit, to Google. And given that scientific publishers probably view the alternative as their work being scraped without an agreement, “I think there will be more of these deals to come,” says Wang.

Information secrets

Some AI developers, such as the Large-scale Artificial Intelligence Network, intentionally keep their data sets open, but many firms developing generative-AI models have kept much of their training data secret, says Baack. “We have no idea what is in there,” he says. Open-source repositories such as arXiv and the scholarly database PubMed of abstracts are thought to be “very popular” sources, he says, although paywalled journal articles probably have their free-to-read abstracts scraped by big technology firms. “They are always on the hunt for that kind of stuff,” he adds.

Proving that an LLM has used any individual paper is difficult, says Yves-Alexandre de Montjoye, a computer scientist at Imperial College London. One way is to prompt the model with an unusual sentence from a text and see whether the output matches the next words in the original. If it does, that is good evidence that the paper is in the training set. But if it doesn’t, that doesn’t mean that the paper wasn’t used — not least because developers can code the LLM to filter responses to ensure they don’t match training data too closely. “It takes a lot for this to work,” he says.

Robo-writers: the rise and risks of language-generating AI

Another method to check whether data are in a training set is known as membership inference attack. This relies on the idea that a model will be more confident about its output when it is seeing something that it has seen before. De Montjoye’s team has developed a version of this, called a copyright trap, for LLMs.

To set the trap, the team generates sentences that look plausible but are nonsense, and hides them in a body of work, for example as white text on a white background or in a field that’s displayed as zero width on a webpage. If an LLM is more ‘surprised’ — a measure known as its perplexity — by an unused control sentence than it is by the one hidden in the text, “that is statistical evidence that the traps were seen before”, he says.

Copyright questions

Even if it were possible to prove that an LLM has been trained on a certain text, it is not clear what happens next. Publishers maintain that, if developers use copyrighted text in training and have not sought a licence, that counts as infringement. But a counter legal argument says that LLMs do not copy anything — they harvest information content from training data, which gets broken up, and use their learning to generate new text.

AI is complicating plagiarism. How should scientists respond?

Litigation might help to resolve this. In an ongoing US copyright case that could be precedent-setting, The New York Times is suing Microsoft and ChatGPT’s developer OpenAI in San Francisco, California. The newspaper accuses the firms of using its journalistic content to train their models without permission.

Many academics are happy to have their work included in LLM training data — especially if the models make them more accurate. “I personally don’t mind if I have a chatbot who writes in the style of me,” says Baack. But he acknowledges that his job is not threatened by LLM outputs in the way that those of other professions, such as artists and writers, are.

Individual scientific authors currently have little power if the publisher of their paper decides to sell access to their copyrighted works. For publicly available articles, there is no established means to apportion credit or know whether a text has been used.

Some researchers, including de Montjoye, are frustrated. “We want LLMs, but we still want something that is fair, and I think we’ve not invented what this looks like yet,” he says.

doi: https://doi.org/10.1038/d41586-024-02599-9

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Title: nl2or: solve complex operations research problems using natural language inputs.

Abstract: Operations research (OR) uses mathematical models to enhance decision-making, but developing these models requires expert knowledge and can be time-consuming. Automated mathematical programming (AMP) has emerged to simplify this process, but existing systems have limitations. This paper introduces a novel methodology that uses recent advances in Large Language Model (LLM) to create and edit OR solutions from non-expert user queries expressed using Natural Language. This reduces the need for domain expertise and the time to formulate a problem. The paper presents an end-to-end pipeline, named NL2OR, that generates solutions to OR problems from natural language input, and shares experimental results on several important OR problems.

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