most citedTrustLLM: Trustworthiness in Large Language Models

54 citations · 58 across the 8 of their papers we have counts for

collaborators

8 papers

cs.LG2026

RAPTOR: Role-Aware Private Training for Mixture-of-Experts

Duc Dm, Khai Le-Duc, Nguyen Do +18

Differentially private (DP) fine-tuning methods treat sparse Mixture-of-Experts (MoE) models as a single dense block, ignoring that shared layers see all data while experts only se…

cs.AI2026

AutoResearch AI: Towards AI-Powered Research Automation for Scientific Discovery

Guiyao Tie, Jiawen Shi, Dingjie Song +20

Scientific research is being reshaped by AI systems that move beyond isolated assistance toward longer-horizon workflows spanning literature grounding, hypothesis generation, exper…

cs.CV2026

CFG-Ctrl: Control-Based Classifier-Free Diffusion Guidance

Hanyang Wang, Yiyang Liu, Jiawei Chi +3

Classifier-Free Guidance (CFG) has emerged as a central approach for enhancing semantic alignment in flow-based diffusion models. In this paper, we explore a unified framework call…

cs.CL2024★ 2 cited

MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases

Rithesh Murthy, Liangwei Yang, Juntao Tan +15

The deployment of Large Language Models (LLMs) and Large Multimodal Models (LMMs) on mobile devices has gained significant attention due to the benefits of enhanced privacy, stabil…

cs.CV2024★ 1 cited

MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens

Anas Awadalla, Le Xue, Oscar Lo +11

Multimodal interleaved datasets featuring free-form interleaved sequences of images and text are crucial for training frontier large multimodal models (LMMs). Despite the rapid pro…

cs.CL2024★ 1 cited

FOFO: A Benchmark to Evaluate LLMs' Format-Following Capability

Congying Xia, Chen Xing, Jiangshu Du +5

This paper presents FoFo, a pioneering benchmark for evaluating large language models' (LLMs) ability to follow complex, domain-specific formats, a crucial yet underexamined capabi…

cs.CL2024

Text2Data: Low-Resource Data Generation with Textual Control

Shiyu Wang, Yihao Feng, Tian Lan +6

Natural language serves as a common and straightforward signal for humans to interact seamlessly with machines. Recognizing the importance of this interface, the machine learning c…

cs.CL2024★ 54 cited

TrustLLM: Trustworthiness in Large Language Models

Yue Huang, Lichao Sun, Haoran Wang +67

Large language models (LLMs), exemplified by ChatGPT, have gained considerable attention for their excellent natural language processing capabilities. Nonetheless, these LLMs prese…