10 citations · 23 across the 42 of their papers we have counts for
43 papers
From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms
Jinghao Luo, Yuchen Tian, Chuxue Cao +6
Large Language Model (LLM)-based agents have fundamentally reshaped artificial intelligence by integrating external tools and planning capabilities. While memory mechanisms have em…
Fine-grained Approaches for Confidence Calibration of LLMs in Automated Code Revision
Hong Yi Lin, Chunhua Liu, Haoyu Gao +2
In today's AI-assisted software engineering landscape, developers increasingly depend on LLMs that are highly capable, yet inherently imperfect. The tendency of these models to pro…
A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM
Bo Wang, Jing Ma, Hongzhan Lin +4
Explainable fake news detection aims to assess the veracity of news claims while providing human-friendly explanations. Existing methods incorporating investigative journalism are…
Probabilistic Concept Graph Reasoning for Multimodal Misinformation Detection
Ruichao Yang, Wei Gao, Xiaobin Zhu +5
Multimodal misinformation poses an escalating challenge that often evades traditional detectors, which are opaque black boxes and fragile against new manipulation tactics. We prese…
Towards Comprehensive Stage-wise Benchmarking of Large Language Models in Fact-Checking
Hongzhan Lin, Zixin Chen, Zhiqi Shen +5
Large Language Models (LLMs) are increasingly deployed in real-world fact-checking systems, yet existing evaluations focus predominantly on claim verification and overlook the broa…
DiffCoT: Diffusion-styled Chain-of-Thought Reasoning in LLMs
Shidong Cao, Hongzhan Lin, Yuxuan Gu +2
Chain-of-Thought (CoT) reasoning improves multi-step mathematical problem solving in large language models but remains vulnerable to exposure bias and error accumulation, as early…
Explainable Ethical Assessment on Human Behaviors by Generating Conflicting Social Norms
Yuxi Sun, Wei Gao, Hongzhan Lin +2
Human behaviors are often guided or constrained by social norms, which are defined as shared, commonsense rules. For example, underlying an action ``\textit{report a witnessed crim…
MM-CRITIC: A Holistic Evaluation of Large Multimodal Models as Multimodal Critique
Gailun Zeng, Ziyang Luo, Hongzhan Lin +5
The ability of critique is vital for models to self-improve and serve as reliable AI assistants. While extensively studied in language-only settings, multimodal critique of Large M…
REFLEX: Self-Refining Explainable Fact-Checking via Verdict-Anchored Style Control
Chuyi Kong, Wei Gao, Jing Ma +2
The prevalence of fake news on social media demands automated fact-checking systems to provide accurate verdicts with faithful explanations. However, existing large language model…
MemeArena: Automating Context-Aware Unbiased Evaluation of Harmfulness Understanding for Multimodal Large Language Models
Zixin Chen, Hongzhan Lin, Kaixin Li +3
The proliferation of memes on social media necessitates the capabilities of multimodal Large Language Models (mLLMs) to effectively understand multimodal harmfulness. Existing eval…
EvolProver: Advancing Automated Theorem Proving by Evolving Formalized Problems via Symmetry and Difficulty
Yuchen Tian, Ruiyuan Huang, Xuanwu Wang +6
Large Language Models (LLMs) for formal theorem proving have shown significant promise, yet they often lack generalizability and are fragile to even minor transformations of proble…
AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries?
Yuchen Tian, Kaixin Li, Hao Chen +5
Large Language Models (LLMs) have recently demonstrated strong capabilities in translating natural language into database queries, especially when dealing with complex graph-struct…
AdamMeme: Adaptively Probe the Reasoning Capacity of Multimodal Large Language Models on Harmfulness
Zixin Chen, Hongzhan Lin, Kaixin Li +5
The proliferation of multimodal memes in the social media era demands that multimodal Large Language Models (mLLMs) effectively understand meme harmfulness. Existing benchmarks for…
Exploring the Impact of Personality Traits on Conversational Recommender Systems: A Simulation with Large Language Models
Xiaoyan Zhao, Yang Deng, Wenjie Wang +5
Conversational Recommender Systems (CRSs) engage users in multi-turn interactions to deliver personalized recommendations. The emergence of large language models (LLMs) further enh…
ScreenSpot-Pro: GUI Grounding for Professional High-Resolution Computer Use
Kaixin Li, Ziyang Meng, Hongzhan Lin +5
Recent advancements in Multi-modal Large Language Models (MLLMs) have led to significant progress in developing GUI agents for general tasks such as web browsing and mobile phone u…
FACT-AUDIT: An Adaptive Multi-Agent Framework for Dynamic Fact-Checking Evaluation of Large Language Models
Hongzhan Lin, Yang Deng, Yuxuan Gu +4
Large Language Models (LLMs) have significantly advanced the fact-checking studies. However, existing automated fact-checking evaluation methods rely on static datasets and classif…
Llasa: Scaling Train-Time and Inference-Time Compute for Llama-based Speech Synthesis
Zhen Ye, Xinfa Zhu, Chi-Min Chan +17
Recent advances in text-based large language models (LLMs), particularly in the GPT series and the o1 model, have demonstrated the effectiveness of scaling both training-time and i…
LLM-Enhanced Multiple Instance Learning for Joint Rumor and Stance Detection with Social Context Information
Ruichao Yang, Jing Ma, Wei Gao +1
The proliferation of misinformation, such as rumors on social media, has drawn significant attention, prompting various expressions of stance among users. Although rumor detection…
ProMedTS: A Self-Supervised, Prompt-Guided Multimodal Approach for Integrating Medical Text and Time Series
Shuai Niu, Jing Ma, Hongzhan Lin +6
Large language models (LLMs) have shown remarkable performance in vision-language tasks, but their application in the medical field remains underexplored, particularly for integrat…
Meme Trojan: Backdoor Attacks Against Hateful Meme Detection via Cross-Modal Triggers
Ruofei Wang, Hongzhan Lin, Ziyuan Luo +4
Hateful meme detection aims to prevent the proliferation of hateful memes on various social media platforms. Considering its impact on social environments, this paper introduces a…
ClarityEthic: Explainable Moral Judgment Utilizing Contrastive Ethical Insights from Large Language Models
Yuxi Sun, Wei Gao, Jing Ma +3
With the rise and widespread use of Large Language Models (LLMs), ensuring their safety is crucial to prevent harm to humans and promote ethical behaviors. However, directly assess…
ScratchEval: Are GPT-4o Smarter than My Child? Evaluating Large Multimodal Models with Visual Programming Challenges
Rao Fu, Ziyang Luo, Hongzhan Lin +2
Recent advancements in large multimodal models (LMMs) have showcased impressive code generation capabilities, primarily evaluated through image-to-code benchmarks. However, these b…
Towards Low-Resource Harmful Meme Detection with LMM Agents
Jianzhao Huang, Hongzhan Lin, Ziyan Liu +3
The proliferation of Internet memes in the age of social media necessitates effective identification of harmful ones. Due to the dynamic nature of memes, existing data-driven model…
SHARP: Unlocking Interactive Hallucination via Stance Transfer in Role-Playing LLMs
Chuyi Kong, Ziyang Luo, Hongzhan Lin +4
The advanced role-playing capabilities of Large Language Models (LLMs) have enabled rich interactive scenarios, yet existing research in social interactions neglects hallucination…