3 citations · 5 across the 6 of their papers we have counts for
6 papers
Can AI Master Econometrics? Evidence from Econometrics AI Agent on Expert-Level Tasks
Qiang Chen, Tianyang Han, Jin Li +5
Can AI effectively perform complex econometric analysis traditionally requiring human expertise? This paper evaluates AI agents' capability to master econometrics, focusing on empi…
Personalized Visual Instruction Tuning
Renjie Pi, Jianshu Zhang, Tianyang Han +3
Recent advancements in multimodal large language models (MLLMs) have demonstrated significant progress; however, these models exhibit a notable limitation, which we refer to as "fa…
CoCA: Regaining Safety-awareness of Multimodal Large Language Models with Constitutional Calibration
Jiahui Gao, Renjie Pi, Tianyang Han +5
The deployment of multimodal large language models (MLLMs) has demonstrated remarkable success in engaging in conversations involving visual inputs, thanks to the superior power of…
Strengthening Multimodal Large Language Model with Bootstrapped Preference Optimization
Renjie Pi, Tianyang Han, Wei Xiong +4
Multimodal Large Language Models (MLLMs) excel in generating responses based on visual inputs. However, they often suffer from a bias towards generating responses similar to their…
The Instinctive Bias: Spurious Images lead to Illusion in MLLMs
Tianyang Han, Qing Lian, Rui Pan +5
Large language models (LLMs) have recently experienced remarkable progress, where the advent of multi-modal large language models (MLLMs) has endowed LLMs with visual capabilities,…
MLLM-Protector: Ensuring MLLM's Safety without Hurting Performance
Renjie Pi, Tianyang Han, Jianshu Zhang +6
The deployment of multimodal large language models (MLLMs) has brought forth a unique vulnerability: susceptibility to malicious attacks through visual inputs. This paper investiga…