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Jiuhai Chen

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8 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

ColorBench: Can VLMs See and Understand the Colorful World? A Comprehensive Benchmark for Color Perception, Reasoning, and Robustness

Yijun Liang
Ming Li
Chenrui Fan
Ziyue Li
Dang Nguyen
Kwesi Cobbina
Shweta Bhardwaj
Jiuhai Chen

Color plays an important role in human perception and usually provides critical clues in visual reasoning. However, it is unclear whether and how vision-language models (VLMs) can perceive, understand, and leverage color as humans. This paper introduces ColorBench, an innovative benchmark meticulously crafted to assess the capabilities of VLMs in color understanding, including color perception, reasoning, and robustness. By curating a suite of diverse test scenarios, with grounding in real applications, ColorBench evaluates how these models perceive colors, infer meanings from color-based cues, and maintain consistent performance under varying color transformations. Through an extensive evaluation of 32 VLMs with varying language models and vision encoders, our paper reveals some undiscovered findings: (i) The scaling law (larger models are better) still holds on ColorBench, while the language model plays a more important role than the vision encoder. (ii) However, the performance gaps across models are relatively small, indicating that color understanding has been largely neglected by existing VLMs. (iii) CoT reasoning improves color understanding accuracies and robustness, though they are vision-centric tasks. (iv) Color clues are indeed leveraged by VLMs on ColorBench but they can also mislead models in some tasks. These findings highlight the critical limitations of current VLMs and underscore the need to enhance color comprehension. Our ColorBench can serve as a foundational tool for advancing the study of human-level color understanding of multimodal AI.

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TMLR Journal 2025 Journal Article

Reliable and Responsible Foundation Models

Xinyu Yang
Junlin Han
Rishi Bommasani
Jinqi Luo
Wenjie Qu
Wangchunshu Zhou
Adel Bibi
Xiyao Wang

Foundation models, including Large Language Models (LLMs), Multimodal Large Language Models (MLLMs), Image Generative Models (i.e, Text-to-Image Models and Image-Editing Models), and Video Generative Models, have become essential tools with broad applications across various domains such as law, medicine, education, finance, and beyond. As these models see increasing real-world deployment, ensuring their reliability and responsibility has become critical for academia, industry, and government. This survey addresses the reliable and responsible development of foundation models. We explore critical issues, including bias and fairness, security and privacy, uncertainty, explainability, and distribution shift. Our research also covers model limitations, such as hallucinations, as well as methods like alignment and Artificial Intelligence-Generated Content (AIGC) detection. For each area, we review the current state of the field and outline concrete future research directions. Additionally, we discuss the intersections between these areas, highlighting their connections and shared challenges. We hope our survey fosters the development of foundation models that are not only powerful but also ethical, trustworthy, reliable, and socially responsible.

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TMLR Journal 2024 Journal Article

Graph Neural Networks Formed via Layer-wise Ensembles of Heterogeneous Base Models

Jiuhai Chen
Jonas Mueller
Vassilis N. Ioannidis
Tom Goldstein
David Wipf

Graph Neural Networks (GNNs) with numerical node features and graph structure as inputs have demonstrated superior performance on various semi-supervised learning tasks with graph data. However, the numerical node features utilized by GNNs are commonly extracted from raw data which is of text or tabular (numeric/categorical) type in most real-world applications. The best models for such data types in most standard supervised learning settings with IID (non-graph) data are not simple neural network layers and thus are not easily incorporated into a GNN. Here we propose a robust stacking framework that fuses graph-aware propagation with arbitrary models intended for IID data, which are ensembled and stacked in multiple layers. Our layer-wise framework leverages bagging and stacking strategies to enjoy strong generalization, in a manner which effectively mitigates label leakage and overfitting. Across a variety of graph datasets with tabular/text node features, our method achieves comparable or superior performance relative to both tabular/text and graph neural network models, as well as existing state-of-the-art hybrid strategies that combine the two.

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ICML Conference 2024 Conference Paper

InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models

Lichang Chen
Jiuhai Chen
Tom Goldstein
Heng Huang 0001
Tianyi Zhou 0001

Large language models (LLMs) are instruction followers but the performance varies under different instructions. It is challenging to create the best instruction, especially for black-box LLMs on which backpropagation is forbidden. Instead of directly optimizing the discrete instruction, we optimize a low-dimensional soft prompt applied to an open-source LLM to generate the instruction for the black-box LLM. In each optimization step of the proposed method InstructZero, a soft prompt is converted into an instruction by the open-source LLM, which is then submitted to the black-box LLM for zero-shot evaluation, whose result is sent to Bayesian optimization to produce new soft prompts improving the zero-shot performance. We evaluate InstructZero on different combinations of open-source LLMs and APIs including Vicuna and ChatGPT. InstructZero outperforms SOTA auto-instruction methods across a variety of downstream tasks.

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ICML Conference 2024 Conference Paper

ODIN: Disentangled Reward Mitigates Hacking in RLHF

Lichang Chen
Chen Zhu 0001
Jiuhai Chen
Davit Soselia
Tianyi Zhou 0001
Tom Goldstein
Heng Huang 0001
Mohammad Shoeybi

In this work, we study the issue of reward hacking on the response length, a challenge emerging in Reinforcement Learning from Human Feedback (RLHF) on LLMs. A well-formatted, verbose but less helpful response from the LLMs can often deceive LLMs or even human evaluators and achieve high scores. The same issue also holds for some reward models in RL. To address the challenges in both training and evaluation, we establish a more reliable evaluation protocol for comparing different training configurations, which inspects the trade-off between LLM evaluation score and response length obtained by varying training hyperparameters. Based on this evaluation, we conduct large-scale studies, where the results shed insights into the efficacy of hyperparameters and tricks used in RL on mitigating length bias. We further propose to improve the reward model by jointly training two linear heads to predict the preference, one trained to correlate with length and the other trained to decorrelate with length and therefore focusing more on the actual content. We then discard the length head in RL to ignore the spurious length reward. Experiments demonstrate that our approach eliminates the reward correlation with length, and improves the obtained policy by a significant margin.

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ICML Conference 2023 Conference Paper

GOAT: A Global Transformer on Large-scale Graphs

Kezhi Kong
Jiuhai Chen
John Kirchenbauer
Renkun Ni
C. Bayan Bruss
Tom Goldstein

Graph transformers have been competitive on graph classification tasks, but they fail to outperform Graph Neural Networks (GNNs) on node classification, which is a common task performed on large-scale graphs for industrial applications. Meanwhile, existing GNN architectures are limited in their ability to perform equally well on both homophilious and heterophilious graphs as their inductive biases are generally tailored to only one setting. To address these issues, we propose GOAT, a scalable global graph transformer. In GOAT, each node conceptually attends to all the nodes in the graph and homophily/heterophily relationships can be learnt adaptively from the data. We provide theoretical justification for our approximate global self-attention scheme, and show it to be scalable to large-scale graphs. We demonstrate the competitiveness of GOAT on both heterophilious and homophilious graphs with millions of nodes.

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ICLR Conference 2022 Conference Paper

Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features

Jiuhai Chen
Jonas Mueller 0001
Vassilis N. Ioannidis
Soji Adeshina
Yangkun Wang
Tom Goldstein
David P. Wipf

Many practical modeling tasks require making predictions using tabular data composed of heterogeneous feature types (e.g., text-based, categorical, continuous, etc.). In this setting boosted decision trees and related ensembling techniques generally dominate real-world applications involving iid training/test sets. However, when there are relations between samples and the iid assumption is no longer reasonable, it remains unclear how to incorporate these dependencies within existing boosting pipelines. To this end, we propose a generalized framework for combining boosted trees and more general model ensembling techniques, with graph propagation layers that share node/sample information across edges connecting related samples. And unlike previous efforts to integrate graph-based models with boosting, our approach is anchored to a principled meta loss function such that provable convergence can be guaranteed under relatively mild assumptions. Across a variety of benchmarks involving non-iid graph data with tabular node features, our framework achieves comparable or superior performance.

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ICLR Conference 2022 Conference Paper

Why Propagate Alone? Parallel Use of Labels and Features on Graphs

Yangkun Wang
Jiarui Jin
Weinan Zhang 0001
Yongyi Yang
Jiuhai Chen
Quan Gan
Yong Yu 0001
Zheng Zhang 0001

One of the challenges of graph-based semi-supervised learning over ordinary supervised learning for classification tasks lies in label utilization. The direct use of ground-truth labels in graphs for training purposes can result in a parametric model learning trivial degenerate solutions (e.g., an identity mapping from input to output). In addressing this issue, a label trick has recently been proposed in the literature and applied to a wide range of graph neural network (GNN) architectures, achieving state-of-the-art results on various datasets. The essential idea is to randomly split the observed labels on the graph and use a fraction of them as input to the model (along with original node features), and predict the remaining fraction. Despite its success in enabling GNNs to propagate features and labels simultaneously, this approach has never been analyzed from a theoretical perspective, nor fully explored across certain natural use cases. In this paper, we demonstrate that under suitable settings, this stochastic trick can be reduced to a more interpretable deterministic form, allowing us to better explain its behavior, including an emergent regularization effect, and motivate broader application scenarios. Our experimental results corroborate these analyses while also demonstrating improved node classification performance applying the label trick in new domains.

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