Search Results for author: Qingkai Zeng

Found 25 papers, 15 papers with code

Graph Foundation Models: A Comprehensive Survey

1 code implementation21 May 2025 Zehong Wang, Zheyuan Liu, Tianyi Ma, Jiazheng Li, Zheyuan Zhang, Xingbo Fu, Yiyang Li, Zhengqing Yuan, Wei Song, Yijun Ma, Qingkai Zeng, Xiusi Chen, Jianan Zhao, Jundong Li, Meng Jiang, Pietro Lio, Nitesh Chawla, Chuxu Zhang, Yanfang Ye

Positioned at the intersection of graph learning and general-purpose AI, GFMs are poised to become foundational infrastructure for open-ended reasoning over structured data.

Graph Learning Knowledge Graphs +2

Can Large Language Models Understand Preferences in Personalized Recommendation?

1 code implementation23 Jan 2025 Zhaoxuan Tan, Zinan Zeng, Qingkai Zeng, Zhenyu Wu, Zheyuan Liu, Fengran Mo, Meng Jiang

To address this, we introduce PerRecBench, disassociating the evaluation from these two factors and assessing recommendation techniques on capturing the personal preferences in a grouped ranking manner.

regression

Protecting Privacy in Multimodal Large Language Models with MLLMU-Bench

1 code implementation29 Oct 2024 Zheyuan Liu, Guangyao Dou, Mengzhao Jia, Zhaoxuan Tan, Qingkai Zeng, Yongle Yuan, Meng Jiang

Generative models such as Large Language Models (LLM) and Multimodal Large Language models (MLLMs) trained on massive web corpora can memorize and disclose individuals' confidential and private data, raising legal and ethical concerns.

Language Modeling Language Modelling +3

Enhancing Mathematical Reasoning in LLMs by Stepwise Correction

no code implementations16 Oct 2024 Zhenyu Wu, Qingkai Zeng, Zhihan Zhang, Zhaoxuan Tan, Chao Shen, Meng Jiang

Best-of-N decoding methods instruct large language models (LLMs) to generate multiple solutions, score each using a scoring function, and select the highest scored as the final answer to mathematical reasoning problems.

Mathematical Reasoning

CodeTaxo: Enhancing Taxonomy Expansion with Limited Examples via Code Language Prompts

1 code implementation17 Aug 2024 Qingkai Zeng, Yuyang Bai, Zhaoxuan Tan, Zhenyu Wu, Shangbin Feng, Meng Jiang

Taxonomies play a crucial role in various applications by providing a structural representation of knowledge.

Taxonomy Expansion

A Psychology-based Unified Dynamic Framework for Curriculum Learning

1 code implementation9 Aug 2024 Guangyu Meng, Qingkai Zeng, John P. Lalor, Hong Yu

Leveraging IRT, we propose a Dynamic Data Selection via Model Ability Estimation (DDS-MAE) strategy to schedule the appropriate amount of data during model training.

Large Language Models Can Self-Correct with Key Condition Verification

no code implementations23 May 2024 Zhenyu Wu, Qingkai Zeng, Zhihan Zhang, Zhaoxuan Tan, Chao Shen, Meng Jiang

The condition can be an entity in an open-domain question or a numeric value in a math question, which requires minimal effort (via prompting) to identify.

Arithmetic Reasoning Math +1

ChatEL: Entity Linking with Chatbots

1 code implementation20 Feb 2024 Yifan Ding, Qingkai Zeng, Tim Weninger

Fortunately, Large Language Models (LLMs) like GPT provide a highly-advanced solution to the problems inherent in EL models, but simply naive prompts to LLMs do not work well.

Entity Linking Sentence

EntGPT: Linking Generative Large Language Models with Knowledge Bases

1 code implementation9 Feb 2024 Yifan Ding, Amrit Poudel, Qingkai Zeng, Tim Weninger, Balaji Veeramani, Sanmitra Bhattacharya

Additionally, our instruction tuning method (EntGPT-I) improves micro-F_1 scores by 2. 1% on average in supervised EL tasks and outperforms several baseline models in six Question Answering tasks.

Entity Disambiguation Entity Linking +3

Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning

2 code implementations6 Feb 2024 Zhaoxuan Tan, Qingkai Zeng, Yijun Tian, Zheyuan Liu, Bing Yin, Meng Jiang

OPPU integrates parametric user knowledge in the personal PEFT parameters with non-parametric knowledge from retrieval and profiles, adapting LLMs to user behavior shifts.

parameter-efficient fine-tuning Retrieval

When eBPF Meets Machine Learning: On-the-fly OS Kernel Compartmentalization

no code implementations11 Jan 2024 Zicheng Wang, Tiejin Chen, Qinrun Dai, Yueqi Chen, Hua Wei, Qingkai Zeng

Compartmentalization effectively prevents initial corruption from turning into a successful attack.

Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models

no code implementations19 Oct 2023 Zhihan Zhang, Shuohang Wang, Wenhao Yu, Yichong Xu, Dan Iter, Qingkai Zeng, Yang Liu, Chenguang Zhu, Meng Jiang

Large language models (LLMs) can perform a wide range of tasks by following natural language instructions, without the necessity of task-specific fine-tuning.

Automatic Controllable Product Copywriting for E-Commerce

1 code implementation21 Jun 2022 Xiaojie Guo, Qingkai Zeng, Meng Jiang, Yun Xiao, Bo Long, Lingfei Wu

Automatic product description generation for e-commerce has witnessed significant advancement in the past decade.

Aspect Extraction Language Modeling +3

Traceability Transformed: Generating moreAccurate Links with Pre-Trained BERT Models

1 code implementation8 Feb 2021 Jinfeng Lin, Yalin Liu, Qingkai Zeng, Meng Jiang, Jane Cleland-Huang

In this study, we propose a novel framework called Trace BERT (T-BERT) to generate trace links between source code and natural language artifacts.

Transfer Learning Software Engineering

Tri-Train: Automatic Pre-Fine Tuning between Pre-Training and Fine-Tuning for SciNER

no code implementations Findings of the Association for Computational Linguistics 2020 Qingkai Zeng, Wenhao Yu, Mengxia Yu, Tianwen Jiang, Tim Weninger, Meng Jiang

The training process of scientific NER models is commonly performed in two steps: i) Pre-training a language model by self-supervised tasks on huge data and ii) fine-tune training with small labelled data.

Language Modeling Language Modelling +1

Technical Question Answering across Tasks and Domains

1 code implementation NAACL 2021 Wenhao Yu, Lingfei Wu, Yu Deng, Qingkai Zeng, Ruchi Mahindru, Sinem Guven, Meng Jiang

In this paper, we propose a novel framework of deep transfer learning to effectively address technical QA across tasks and domains.

Question Answering Reading Comprehension +2

Crossing Variational Autoencoders for Answer Retrieval

no code implementations ACL 2020 Wenhao Yu, Lingfei Wu, Qingkai Zeng, Shu Tao, Yu Deng, Meng Jiang

Existing methods learned semantic representations with dual encoders or dual variational auto-encoders.

Retrieval

Faceted Hierarchy: A New Graph Type to Organize Scientific Concepts and a Construction Method

no code implementations WS 2019 Qingkai Zeng, Mengxia Yu, Wenhao Yu, JinJun Xiong, Yiyu Shi, Meng Jiang

On a scientific concept hierarchy, a parent concept may have a few attributes, each of which has multiple values being a group of child concepts.

Face Recognition

Cannot find the paper you are looking for? You can Submit a new open access paper.