Papers about scientific hypothesis generation with large language models (LLMs).
Papers about scientific hypothesis generation with large language models (LLMs).
🔬 About Us: Welcome to LLM-SCI-GEN, your go-to repository for papers on scientific hypothesis generation using large language models (LLMs). Our aim is to curate and maintain a comprehensive collection of research in this dynamic field, ensuring that the latest advancements and insights are accessible to researchers, practitioners, and enthusiasts alike.
📚 Contribute: We encourage you to recommend any missing papers! Your contributions help us keep our repository up-to-date and valuable for the community. You can submit recommendations through Adding Issues or Pull Requests. Your participation is greatly appreciated and vital to the growth and accuracy of our collection.
📝 Details and Classification: For a detailed summary and classification of papers, please refer to our wiki. Here, you'll find organized information that helps you navigate the extensive research in this field.
Thank you for being a part of our community and contributing to the advancement of knowledge in this exciting field!
The repo is maintained by myself only for now. Feel free to connect with me on LinkedIn if you'd like to discuss new ideas or share your thoughts about this field: https://www.linkedin.com/in/agpphd/ .
- [Link] Artificial intelligence to support publishing and peer review: A summary and review.
2023 - [arxiv] Large Language Models as Biomedical Hypothesis Generators: A Comprehensive Evaluation
2024
- [arxiv] Large Language Models are Zero Shot Hypothesis Proposers.
2023 - [Link] Machine learning for hypothesis generation in biology and medicine: exploring the latent space of neuroscience and developmental bioelectricity.
2024 - [arxiv] Automating Psychological Hypothesis Generation with AI: Large Language Models Meet Causal Graph.
2024 - [arxiv] Artificial muses: Generative Artificial Intelligence Chatbots Have Risen to Human-Level Creativity.
2023 - [Link] Ideas are Dimes a Dozen: Large Language Models for Idea Generation in Innovation.
2023 - [arxiv] Automated Statistical Model Discovery with Language Models.
2024 - [arxiv] Hypothesis Search: Inductive Reasoning with Language Models.
2023 - [arxiv] Phenomenal Yet Puzzling: Testing Inductive Reasoning Capabilities of Language Models with Hypothesis Refinement.
2023 - [arxiv] Can ChatGPT be used to generate scientific hypotheses?
2023 - [arxiv] Harnessing the Power of Adversarial Prompting and Large Language Models for Robust Hypothesis Generation in Astronomy
2023 - [arxiv] ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models
2024 - [biorxiv] Replicating a High-Impact Scientific Publication Using Systems of Large Language Models
2024 - [arxiv] Scientific Hypothesis Generation by a Large Language Model: Laboratory Validation in Breast Cancer Treatment
2024 - [arxiv] Autonomous LLM-driven research from data to human-verifiable research papers
2024 - [arxiv] Can LLMs Generate Novel Research Ideas?
2024 - [Link] Language Agents Achieve Superhuman Synthesis of Scientific Knowledge
2024 - [arxiv] Hypothesis Generation with Large Language Models
2024 - [arxiv] Scideator: Human-LLM Scientific Idea Generation Grounded in Research-Paper Facet Recombination
2024 - [arxiv] DiscoveryWorld: A Virtual Environment for Developing and Evaluating Automated Scientific Discovery Agents
2024
- [arxiv] Extracting Self-Consistent Causal Insights from Users Feedback with LLMs and In-context Learning.
2023
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📄 Add a new paper or update an existing one related to hypothesis generation with LLMs. This ensures our repository remains comprehensive and up-to-date with the latest advancements and research in the field.
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📋 Follow the format of existing entries to describe the work. Consistency is crucial for maintaining a professional and organized repository, making it easier for users to navigate and understand the contributions.
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💡 Optionally, include a brief explanation of why the paper should be added or updated. This can provide valuable context and justification for the addition or update, helping reviewers understand its significance. You can submit your explanations and contributions via Adding Issues or Pull Requests on our platform.
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🔍 Double-check for accuracy and completeness. Ensuring the information is accurate and comprehensive helps maintain the quality and reliability of the repository, making it a valuable resource for all users.
Check out other repos related to papers about LLMs:
- https://github.com/zjukg/KG-LLM-Papers: Papers integrating knowledge graphs (KGs) and large language models (LLMs)