Large Language Model
Supplementary Materials for M
The attribute extractions of these notes were then verified for by authors of this paper.
Emergence of Hidden Capabilities: Exploring Learning Dynamics in Concept Space Core Francisco Park
To address these questions, we propose analyzing a model's learning
Hybrid Generative AI for De Novo Design of Co-Crystals with Enhanced Tabletability
Furthermore, we explore the potential of language models in generating co-crystals. Finally, we present numerous previously unknown co-crystals predicted by GEM-CODE and discuss its potential in accelerating drug development.
Enhancing Efficiency of Generated Code via Self-Optimization Dong Huang
These profiles are fed back to the LLM, which then revises the code to reduce overhead.
FAST opic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic Model Xiaobao Wu1 Thong Nguyen
Topic models have been evolving rapidly over the years, from conventional to recent neural models.
Understanding and Minimising Outlier Features in Transformer Training Bobby He
Despite their widespread use, our understanding of deep neural networks (NNs) and their training dynamics is very much incomplete.
Cross-model Control: Improving Multiple Large Language Models in One-time Training
However, how to reuse the fine-tuning outcomes of one model to other models to reduce training costs remains a challenge.
Multi-language Diversity Benefits Autoformalization
Autoformalization is the task of translating natural language materials into machine-verifiable formalisations. Progress in autoformalization research is hindered by the lack of a sizeable dataset consisting of informal-formal pairs expressing the same essence.
