Search Results for author: Yunshi Lan

Found 25 papers, 14 papers with code

Aligning Large Language Models to a Domain-specific Graph Database

no code implementations26 Feb 2024 Yuanyuan Liang, Keren Tan, Tingyu Xie, Wenbiao Tao, Siyuan Wang, Yunshi Lan, Weining Qian

Graph Databases (Graph DB) are widely applied in various fields, including finance, social networks, and medicine.

Unsupervised Text Style Transfer via LLMs and Attention Masking with Multi-way Interactions

no code implementations21 Feb 2024 Lei Pan, Yunshi Lan, Yang Li, Weining Qian

Among existing methods for UTST tasks, attention masking approach and Large Language Models (LLMs) are deemed as two pioneering methods.

In-Context Learning Knowledge Distillation +4

TreeEval: Benchmark-Free Evaluation of Large Language Models through Tree Planning

1 code implementation20 Feb 2024 Xiang Li, Yunshi Lan, Chao Yang

Recently, numerous new benchmarks have been established to evaluate the performance of large language models (LLMs) via either computing a holistic score or employing another LLM as a judge.

Question Generation Question-Generation

Safety of Multimodal Large Language Models on Images and Text

1 code implementation1 Feb 2024 Xin Liu, Yichen Zhu, Yunshi Lan, Chao Yang, Yu Qiao

In this paper, we systematically survey current efforts on the evaluation, attack, and defense of MLLMs' safety on images and text.

MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models

1 code implementation29 Nov 2023 Xin Liu, Yichen Zhu, Jindong Gu, Yunshi Lan, Chao Yang, Yu Qiao

The security concerns surrounding Large Language Models (LLMs) have been extensively explored, yet the safety of Multimodal Large Language Models (MLLMs) remains understudied.

R$^3$ Prompting: Review, Rephrase and Resolve for Chain-of-Thought Reasoning in Large Language Models under Noisy Context

no code implementations25 Oct 2023 Qingyuan Tian, Hanlun Zhu, Lei Wang, Yang Li, Yunshi Lan

More analyses and ablation studies show the robustness and generalization of R$^3$ prompting method in solving reasoning tasks in LLMs under noisy context.


Prompting Large Language Models with Chain-of-Thought for Few-Shot Knowledge Base Question Generation

no code implementations12 Oct 2023 Yuanyuan Liang, Jianing Wang, Hanlun Zhu, Lei Wang, Weining Qian, Yunshi Lan

Inspired by Chain-of-Thought (CoT) prompting, which is an in-context learning strategy for reasoning, we formulate KBQG task as a reasoning problem, where the generation of a complete question is splitted into a series of sub-question generation.

In-Context Learning Question Generation +1

Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models

3 code implementations6 May 2023 Lei Wang, Wanyu Xu, Yihuai Lan, Zhiqiang Hu, Yunshi Lan, Roy Ka-Wei Lee, Ee-Peng Lim

To address the calculation errors and improve the quality of generated reasoning steps, we extend PS prompting with more detailed instructions and derive PS+ prompting.


GradMA: A Gradient-Memory-based Accelerated Federated Learning with Alleviated Catastrophic Forgetting

1 code implementation CVPR 2023 Kangyang Luo, Xiang Li, Yunshi Lan, Ming Gao

Federated Learning (FL) has emerged as a de facto machine learning area and received rapid increasing research interests from the community.

Continual Learning Federated Learning +1

Improving Compositional Generalization in Math Word Problem Solving

1 code implementation3 Sep 2022 Yunshi Lan, Lei Wang, Jing Jiang, Ee-Peng Lim

To improve the compositional generalization in MWP solving, we propose an iterative data augmentation method that includes diverse compositional variation into training data and could collaborate with MWP methods.

Data Augmentation Math +1

Modeling Transitions of Focal Entities for Conversational Knowledge Base Question Answering

1 code implementation ACL 2021 Yunshi Lan, Jing Jiang

We propose a novel graph-based model to capture the transitions of focal entities and apply a graph neural network to derive a probability distribution of focal entities for each question, which is then combined with a standard KBQA module to perform answer ranking.

Knowledge Base Question Answering

Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision Signals

1 code implementation11 Jan 2021 Gaole He, Yunshi Lan, Jing Jiang, Wayne Xin Zhao, Ji-Rong Wen

In our approach, the student network aims to find the correct answer to the query, while the teacher network tries to learn intermediate supervision signals for improving the reasoning capacity of the student network.

Knowledge Base Question Answering Semantic Parsing

Query Graph Generation for Answering Multi-hop Complex Questions from Knowledge Bases

no code implementations ACL 2020 Yunshi Lan, Jing Jiang

Previous work on answering complex questions from knowledge bases usually separately addresses two types of complexity: questions with constraints and questions with multiple hops of relations.

Graph Generation

Embedding WordNet Knowledge for Textual Entailment

no code implementations COLING 2018 Yunshi Lan, Jing Jiang

In this paper, we study how we can improve a deep learning approach to textual entailment by incorporating lexical entailment relations from WordNet.

Feature Engineering Lexical Entailment +1

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