Search Results for author:
Found 11 papers, 4 papers with code
VisLingInstruct: Elevating Zero-Shot Learning in Multi-Modal Language Models with Autonomous Instruction Optimization
This paper presents VisLingInstruct, a novel approach to advancing Multi-Modal Language Models (MMLMs) in zero-shot learning.
CRNNet: Copy Recurrent Neural Network Structure Network
We propose a copy module to detect complicating diseases; by the proposed copy module and the adversarial learning strategy, we identify complicating diseases efficiently.
Just-in-Time Security Patch Detection -- LLM At the Rescue for Data Augmentation
To address this pressing issue, we introduce a novel security patch detection system, LLMDA, which capitalizes on Large Language Models (LLMs) and code-text alignment methodologies for patch review, data enhancement, and feature combination.
Copy Recurrent Neural Network Structure Network
Electronic Health Record (EHR) coding involves automatically classifying EHRs into diagnostic codes.
Is ChatGPT the Ultimate Programming Assistant -- How far is it?
To assess the feasibility of using an LLM as a useful assistant bot for programmers, we must assess its realistic capabilities on unseen problems as well as its capabilities on various tasks.
GE-Blender: Graph-Based Knowledge Enhancement for Blender
Although the great success of open-domain dialogue generation, unseen entities can have a large impact on the dialogue generation task.
App Review Driven Collaborative Bug Finding
Our novelty is that we guide the bug finding process by considering that existing bugs have been hinted within app reviews.
CKG: Dynamic Representation Based on Context and Knowledge Graph
Recently, neural language representation models pre-trained on large corpus can capture rich co-occurrence information and be fine-tuned in downstream tasks to improve the performance.
Moto: Enhancing Embedding with Multiple Joint Factors for Chinese Text Classification
Recently, language representation techniques have achieved great performances in text classification.
Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness
To tackle this problem, our intuition is that natural language processing can provide the necessary representations and models for assessing the semantic correlation between a bug (question) and a patch (answer).
MetaTPTrans: A Meta Learning Approach for Multilingual Code Representation Learning
Representation learning of source code is essential for applying machine learning to software engineering tasks.
