most citedGraphCoder: Enhancing Repository-Level Code Completion via Code Context Graph-based Retrieval and Language Model

5 citations · 9 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CL2024

Exploring LLM-based Data Annotation Strategies for Medical Dialogue Preference Alignment

Chengfeng Dou, Ying Zhang, Zhi Jin +4

This research examines the use of Reinforcement Learning from AI Feedback (RLAIF) techniques to improve healthcare dialogue models, with the aim of tackling the challenges of prefe…

cs.AI2024

A new approach for encoding code and assisting code understanding

Mengdan Fan, Wei Zhang, Haiyan Zhao +1

Some companies (e.g., Microsoft Research and Google DeepMind) have discovered some of the limitations of GPTs' autoregressive paradigm next-word prediction, manifested in the model…

cs.SE2024★ 5 cited

GraphCoder: Enhancing Repository-Level Code Completion via Code Context Graph-based Retrieval and Language Model

Wei Liu, Ailun Yu, Daoguang Zan +5

The performance of repository-level code completion depends upon the effective leverage of both general and repository-specific knowledge. Despite the impressive capability of code…

cs.CL2024

EVIT: Event-Oriented Instruction Tuning for Event Reasoning

Zhengwei Tao, Xiancai Chen, Zhi Jin +3

Events refer to specific occurrences, incidents, or happenings that take place under a particular background. Event reasoning aims to infer events according to certain relations an…

cs.AI2024

MEEL: Multi-Modal Event Evolution Learning

Zhengwei Tao, Zhi Jin, Junqiang Huang +5

Multi-modal Event Reasoning (MMER) endeavors to endow machines with the ability to comprehend intricate event relations across diverse data modalities. MMER is fundamental and unde…

cs.CL2024

Integrating Physician Diagnostic Logic into Large Language Models: Preference Learning from Process Feedback

Chengfeng Dou, Zhi Jin, Wenpin Jiao +3

The use of large language models in medical dialogue generation has garnered significant attention, with a focus on improving response quality and fluency. While previous studies h…

cs.AI2023★ 2 cited

Enhancing the Spatial Awareness Capability of Multi-Modal Large Language Model

Yongqiang Zhao, Zhenyu Li, Zhi Jin +6

The Multi-Modal Large Language Model (MLLM) refers to an extension of the Large Language Model (LLM) equipped with the capability to receive and infer multi-modal data. Spatial awa…

cs.CL2023★ 2 cited

PlugMed: Improving Specificity in Patient-Centered Medical Dialogue Generation using In-Context Learning

Chengfeng Dou, Zhi Jin, Wenping Jiao +3

The patient-centered medical dialogue systems strive to offer diagnostic interpretation services to users who are less knowledgeable about medical knowledge, through emphasizing th…