CV

Master of Science

Fudan University

Computer Science

Supervised by Yanghua Xiao

Bachelor of Science

Fudan University

Computer Science

Supervised by Yanghua Xiao

Research Intern

LUKA Group

Supervised by Muhao Chen

Research Intern

OSU NLP Group

Supervised by Yu Su

Research Intern

Meituan

Supervised by Rui Xie

National Scholarship

Unraveling Cross-Modality Knowledge Conflicts in Large Vision-Language Models

arxiv

We formally define the problem of cross-modality parametric knowledge conflicts by systematically detecing, interpreting, and mitigating them.

From Persona to Personalization: A Survey on Role-Playing Language Agents

TMLR

A survey on Role-Playing Language Agents.

How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?

COLM 2024

We present a comprehensive investigation into the robustness of LLMs to different types of irrelevant information under various conditions.

TravelPlanner: A Benchmark for Real-World Planning with Language Agents

ICML 2024 Spotlight

We propose TravelPlanner, a new planning benchmark that focuses on travel planning, a common real-world planning scenario. It provides a rich sandbox environment, various tools for accessing nearly four million data records, and 1,225 meticulously curated planning intents and reference plans.

Deductive Beam Search: Decoding Deducible Rationale for Chain-of-Thought Reasoning

COLM 2024

Deductive Beam Search, constrained by the principle of deductive reasoning, integrates CoT with step-wise beam search to guide reasoning towards a deducible path.

Towards Visual Taxonomy Expansion

ACMMM 2023

We propose Visual Taxonomy Expansion (VTE), introducing visual features into the taxonomy expansion task.

SLR:A Million-Scale Comprehensive Crossword Dataset for Simultaneous Learning and Reasoning

Neurocomputing

We propose a novel crossword-based NLU task that imparts knowledge information to a model by solving crossword clues and simultaneously trains the model to infer new knowledge from existing knowledge.

End-to-End Entity Linking with Hierarchical Reinforcement Learning

AAAI 2023

We propose to model the Entity Linking task as a hierarchical decision-making process and design a hierarchical reinforcement learning algorithm to solve the problem.