Search Results for author:
Found 9 papers, 5 papers with code
AS-ES Learning: Towards Efficient CoT Learning in Small Models
Chain-of-Thought (CoT) serves as a critical emerging ability in LLMs, especially when it comes to logical reasoning.
Beyond Direct Diagnosis: LLM-based Multi-Specialist Agent Consultation for Automatic Diagnosis
Automatic diagnosis is a significant application of AI in healthcare, where diagnoses are generated based on the symptom description of patients.
From Artificially Real to Real: Leveraging Pseudo Data from Large Language Models for Low-Resource Molecule Discovery
Furthermore, our method shows a sustained improvement as the volume of pseudo data increases, revealing the great potential of pseudo data in advancing low-resource cross-modal molecule discovery.
Knowledge-tuning Large Language Models with Structured Medical Knowledge Bases for Reliable Response Generation in Chinese
To address this challenge, we propose knowledge-tuning, which leverages structured medical knowledge bases for the LLMs to grasp domain knowledge efficiently and facilitate reliable response generation.
Manifold-based Verbalizer Space Re-embedding for Tuning-free Prompt-based Classification
Experimental results indicate that even without tuning any parameters, our LLE-INC is on par with automated verbalizers with parameter tuning.
UniCoRN: Unified Cognitive Signal ReconstructioN bridging cognitive signals and human language
In this paper, we propose fMRI2text, the first openvocabulary task aiming to bridge fMRI time series and human language.
HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge
Large Language Models (LLMs), such as the LLaMA model, have demonstrated their effectiveness in various general-domain natural language processing (NLP) tasks.
Global Prompt Cell: A Portable Control Module for Effective Prompt Tuning
As a novel approach to tuning pre-trained models, prompt tuning involves freezing the parameters in downstream tasks while inserting trainable embeddings into inputs in the first layer.
Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words
Prompt-based fine-tuning for pre-trained models has proven effective for many natural language processing tasks under few-shot settings in general domain.
