Search Results for author:
Found 17 papers, 9 papers with code
MIMIR: A Streamlined Platform for Personalized Agent Tuning in Domain Expertise
Recently, large language models (LLMs) have evolved into interactive agents, proficient in planning, tool use, and task execution across a wide variety of tasks.
A Survey of Generative AI for De Novo Drug Design: New Frontiers in Molecule and Protein Generation
Within each theme, we identify a variety of subtasks and applications, highlighting important datasets, benchmarks, and model architectures and comparing the performance of top models.
ChatCell: Facilitating Single-Cell Analysis with Natural Language
As Large Language Models (LLMs) rapidly evolve, their influence in science is becoming increasingly prominent.
Prioritizing Safeguarding Over Autonomy: Risks of LLM Agents for Science
Intelligent agents powered by large language models (LLMs) have demonstrated substantial promise in autonomously conducting experiments and facilitating scientific discoveries across various disciplines.
Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agents
Large language models (LLMs) have dramatically enhanced the field of language intelligence, as demonstrably evidenced by their formidable empirical performance across a spectrum of complex reasoning tasks.
MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning
Large language models (LLMs), despite their remarkable progress across various general domains, encounter significant barriers in medicine and healthcare.
Investigating Data Contamination in Modern Benchmarks for Large Language Models
Recent observations have underscored a disparity between the inflated benchmark scores and the actual performance of LLMs, raising concerns about potential contamination of evaluation benchmarks.
ML-Bench: Evaluating Large Language Models for Code Generation in Repository-Level Machine Learning Tasks
While Large Language Models (LLMs) have demonstrated proficiency in code generation benchmarks, translating these results into practical development scenarios - where leveraging existing repository-level libraries is the norm - remains challenging.
Struc-Bench: Are Large Language Models Really Good at Generating Complex Structured Data?
Despite the remarkable capabilities of Large Language Models (LLMs) like GPT-4, producing complex, structured tabular data remains challenging.
BioCoder: A Benchmark for Bioinformatics Code Generation with Contextual Pragmatic Knowledge
This is evident from the performance gain of GPT-3. 5/4 compared to the smaller models on the benchmark (50% vs up to ~25%).
ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs
Based on ToolBench, we fine-tune LLaMA to obtain an LLM ToolLLaMA, and equip it with a neural API retriever to recommend appropriate APIs for each instruction.
Ranked #3 on
Trajectory Planning
on ToolBench
GersteinLab at MEDIQA-Chat 2023: Clinical Note Summarization from Doctor-Patient Conversations through Fine-tuning and In-context Learning
This paper presents our contribution to the MEDIQA-2023 Dialogue2Note shared task, encompassing both subtask A and subtask B.
Scalable privacy-preserving cancer type prediction with homomorphic encryption
Privacy concerns in outsourced ML, especially in the field of genetics, motivate the use of encrypted computation, like Homomorphic Encryption (HE).
Higher-Order Generalization Bounds: Learning Deep Probabilistic Programs via PAC-Bayes Objectives
Here, we offer a framework for representing and learning flexible PAC-Bayes bounds as stochastic programs using DPP-based methods.
Forest Fire Clustering for Single-cell Sequencing with Iterative Label Propagation and Parallelized Monte Carlo Simulation
Here, we introduce Forest Fire Clustering, an efficient and interpretable method for cell-type discovery from single-cell data.
Hybrid Quantum-Classical Stochastic Networks with Boltzmann Layers
Quantum Machine Learning (QML) has the potential to significantly advance the state-of-the-art in artificial intelligence, due to recent developments in quantum computing hardware and algorithm design.
Rank Projection Trees for Multilevel Neural Network Interpretation
A variety of methods have been proposed for interpreting nodes in deep neural networks, which typically involve scoring nodes at lower layers with respect to their effects on the output of higher-layer nodes (where lower and higher layers are closer to the input and output layers, respectively).
