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Found 13 papers, 6 papers with code
DARWIN 1.5: Large Language Models as Materials Science Adapted Learners
It further outperforms traditional machine learning models on various tasks in material science, showcasing the potential of LLMs to provide a more versatile and scalable foundation model for materials discovery and design.
Does Few-Shot Learning Help LLM Performance in Code Synthesis?
This work focuses on the few-shot examples present in most code generation prompts, offering a systematic study on whether few-shot examples improve LLM's coding capabilities, which few-shot examples have the largest impact, and how to select impactful examples.
ByteScience: Bridging Unstructured Scientific Literature and Structured Data with Auto Fine-tuned Large Language Model in Token Granularity
Natural Language Processing (NLP) is widely used to supply summarization ability from long context to structured information.
From Tokens to Materials: Leveraging Language Models for Scientific Discovery
Exploring the predictive capabilities of language models in material science is an ongoing interest.
MOOSE-Chem: Large Language Models for Rediscovering Unseen Chemistry Scientific Hypotheses
In this work, we investigate this central research question: Can LLMs automatically discover novel and valid chemistry research hypotheses given only a chemistry research background (consisting of a research question and/or a background survey), without limitation on the domain of the research question?
LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning
This paper presents an advanced mathematical problem-solving framework, LLaMA-Berry, for enhancing the mathematical reasoning ability of Large Language Models (LLMs).
SciQAG: A Framework for Auto-Generated Science Question Answering Dataset with Fine-grained Evaluation
SciQAG consists of a QA generator and a QA evaluator, which work together to extract diverse and research-level questions and answers from scientific papers.
Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model
By implementing network-based algorithms, MKG not only facilitates efficient link prediction but also significantly reduces reliance on traditional experimental methods.
ASCEND: Accurate yet Efficient End-to-End Stochastic Computing Acceleration of Vision Transformer
To overcome the compatibility challenges, ASCEND proposes a novel deterministic SC block for GELU and leverages an SC-friendly iterative approximate algorithm to design an accurate and efficient softmax circuit.
Data Attribution for Diffusion Models: Timestep-induced Bias in Influence Estimation
Data attribution methods trace model behavior back to its training dataset, offering an effective approach to better understand ''black-box'' neural networks.
DARWIN Series: Domain Specific Large Language Models for Natural Science
To add new capabilities in natural science, enabling the acceleration and enrichment of automation of the discovery process, we present DARWIN, a series of tailored LLMs for natural science, mainly in physics, chemistry, and material science.
Large Language Models as Master Key: Unlocking the Secrets of Materials Science with GPT
The amount of data has growing significance in exploring cutting-edge materials and a number of datasets have been generated either by hand or automated approaches.
Interdisciplinary Discovery of Nanomaterials Based on Convolutional Neural Networks
The material science literature contains up-to-date and comprehensive scientific knowledge of materials.
