Search Results for author:
Found 8 papers, 4 papers with code
TOMG-Bench: Evaluating LLMs on Text-based Open Molecule Generation
In this paper, we propose Text-based Open Molecule Generation Benchmark (TOMG-Bench), the first benchmark to evaluate the open-domain molecule generation capability of LLMs.
Ranked #1 on
Description-guided molecule generation
on TOMG-Bench
MolReFlect: Towards In-Context Fine-grained Alignments between Molecules and Texts
Previous endeavours often treat the molecule as a general SMILES string or molecular graph, neglecting the fine-grained alignments between the molecular sub-structures and the descriptive textual phrases, which are crucial for accurate and explainable predictions.
MolReFlect: Towards Fine-grained In-Context Alignment between Molecules and Texts
Previous endeavours often treat the molecule as a general SMILES string or molecular graph, neglecting the fine-grained alignments between the molecular sub-structures and the descriptive textual phrases, which are crucial for accurate and explainable predictions.
Ranked #1 on
Molecule Captioning
on ChEBI-20
Recommender Systems in the Era of Large Language Models (LLMs)
As a result, recent studies have attempted to harness the power of LLMs to enhance recommender systems.
Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Models: A ChatGPT Perspective
In this work, we propose a novel LLM-based framework (MolReGPT) for molecule-caption translation, where an In-Context Few-Shot Molecule Learning paradigm is introduced to empower molecule discovery with LLMs like ChatGPT to perform their in-context learning capability without domain-specific pre-training and fine-tuning.
Ranked #5 on
Text-based de novo Molecule Generation
on ChEBI-20
Improving User Controlled Table-To-Text Generation Robustness
In this work we study user controlled table-to-text generation where users explore the content in a table by selecting cells and reading a natural language description thereof automatically produce by a natural language generator.
Generative Diffusion Models on Graphs: Methods and Applications
Given the great success of diffusion models in image generation, increasing efforts have been made to leverage these techniques to advance graph generation in recent years.
Satellite Image Small Target Application Based on Deep Segmented Residual Neural Network
This study employs a deep segmented residual neural network model to analyze the super-resolution of a single satellite image.
