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Jinqi Luo

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6 papers

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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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NeurIPS Conference 2025 Conference Paper

SECA: Semantically Equivalent and Coherent Attacks for Eliciting LLM Hallucinations

Buyun Liang
Liangzu Peng
Jinqi Luo
Darshan Thaker
Kwan Ho Ryan Chan
Rene Vidal

Large Language Models (LLMs) are increasingly deployed in high-risk domains. However, state-of-the-art LLMs often exhibit hallucinations, raising serious concerns about their reliability. Prior work has explored adversarial attacks to elicit hallucinations in LLMs, but these methods often rely on unrealistic prompts, either by inserting nonsensical tokens or by altering the original semantic intent. Consequently, such approaches provide limited insight into how hallucinations arise in real-world settings. In contrast, adversarial attacks in computer vision typically involve realistic modifications to input images. However, the problem of identifying realistic adversarial prompts for eliciting LLM hallucinations remains largely underexplored. To address this gap, we propose Semantically Equivalent and Coherent Attacks (SECA), which elicit hallucinations via realistic modifications to the prompt that preserve its meaning while maintaining semantic coherence. Our contributions are threefold: (i) we formulate finding realistic attacks for hallucination elicitation as a constrained optimization problem over the input prompt space under semantic equivalence and coherence constraints; (ii) we introduce a constraint-preserving zeroth-order method to effectively search for adversarial yet feasible prompts; and (iii) we demonstrate through experiments on open-ended multiple-choice question answering tasks that SECA achieves higher attack success rates while incurring almost no semantic equivalence or semantic coherence errors compared to existing methods. SECA highlights the sensitivity of both open-source and commercial gradient-inaccessible LLMs to realistic and plausible prompt variations. Code is available at https: //github. com/Buyun-Liang/SECA.

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NeurIPS Conference 2024 Conference Paper

LogiCity: Advancing Neuro-Symbolic AI with Abstract Urban Simulation

Bowen Li
Zhaoyu Li
Qiwei Du
Jinqi Luo
Wenshan Wang
Yaqi Xie
Simon Stepputtis
Chen Wang

Recent years have witnessed the rapid development of Neuro-Symbolic (NeSy) AI systems, which integrate symbolic reasoning into deep neural networks. However, most of the existing benchmarks for NeSy AI fail to provide long-horizon reasoning tasks with complex multi-agent interactions. Furthermore, they are usually constrained by fixed and simplistic logical rules over limited entities, making them far from real-world complexities. To address these crucial gaps, we introduce LogiCity, the first simulator based on customizable first-order logic (FOL) for an urban-like environment with multiple dynamic agents. LogiCity models diverse urban elements using semantic and spatial concepts, such as $\texttt{IsAmbulance}(\texttt{X})$ and $\texttt{IsClose}(\texttt{X}, \texttt{Y})$. These concepts are used to define FOL rules that govern the behavior of various agents. Since the concepts and rules are abstractions, they can be universally applied to cities with any agent compositions, facilitating the instantiation of diverse scenarios. Besides, a key feature of LogiCity is its support for user-configurable abstractions, enabling customizable simulation complexities for logical reasoning. To explore various aspects of NeSy AI, LogiCity introduces two tasks, one features long-horizon sequential decision-making, and the other focuses on one-step visual reasoning, varying in difficulty and agent behaviors. Our extensive evaluation reveals the advantage of NeSy frameworks in abstract reasoning. Moreover, we highlight the significant challenges of handling more complex abstractions in long-horizon multi-agent scenarios or under high-dimensional, imbalanced data. With its flexible design, various features, and newly raised challenges, we believe LogiCity represents a pivotal step forward in advancing the next generation of NeSy AI. All the code and data are open-sourced at our website.

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NeurIPS Conference 2024 Conference Paper

PaCE: Parsimonious Concept Engineering for Large Language Models

Jinqi Luo
Tianjiao Ding
Kwan H. Chan
Darshan Thaker
Aditya Chattopadhyay
Chris Callison-Burch
René Vidal

Large Language Models (LLMs) are being used for a wide variety of tasks. While they are capable of generating human-like responses, they can also produce undesirable output including potentially harmful information, racist or sexist language, and hallucinations. Alignment methods are designed to reduce such undesirable output, via techniques such as fine-tuning, prompt engineering, and representation engineering. However, existing methods face several challenges: some require costly fine-tuning for every alignment task; some do not adequately remove undesirable concepts, failing alignment; some remove benign concepts, lowering the linguistic capabilities of LLMs. To address these issues, we propose Parsimonious Concept Engineering (PaCE), a novel activation engineering framework for alignment. First, to sufficiently model the concepts, we construct a large-scale concept dictionary in the activation space, in which each atom corresponds to a semantic concept. Given any alignment task, we instruct a concept partitioner to efficiently annotate the concepts as benign or undesirable. Then, at inference time, we decompose the LLM activations along the concept dictionary via sparse coding, to accurately represent the activations as linear combinations of benign and undesirable components. By removing the latter ones from the activations, we reorient the behavior of the LLM towards the alignment goal. We conduct experiments on tasks such as response detoxification, faithfulness enhancement, and sentiment revising, and show that PaCE achieves state-of-the-art alignment performance while maintaining linguistic capabilities.

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AAAI Conference 2021 Short Paper

Generating Adversarial yet Inconspicuous Patches with a Single Image (Student Abstract)

Jinqi Luo
Tao Bai
Jun Zhao

Deep neural networks have been shown vulnerable to adversarial patches, where exotic patterns can result in model’s wrong prediction. Nevertheless, existing approaches to adversarial patch generation hardly consider the contextual consistency between patches and the image background, causing such patches to be easily detected by human observation. Additionally, these methods require a large amount of data for training, which is computationally expensive. To overcome these challenges, we propose an approach to generate adversarial yet inconspicuous patches with one single image. In our approach, adversarial patches are produced in a coarse-to-fine way with multiple scales of generators and discriminators. The selection of patch location is based on the perceptual sensitivity of victim models. Contextual information is encoded during the Min-Max training to make patches consistent with surroundings.

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IJCAI Conference 2021 Conference Paper

Recent Advances in Adversarial Training for Adversarial Robustness

Tao Bai
Jinqi Luo
Jun Zhao
Bihan Wen
Qian Wang

Adversarial training is one of the most effective approaches for deep learning models to defend against adversarial examples. Unlike other defense strategies, adversarial training aims to enhance the robustness of models intrinsically. During the past few years, adversarial training has been studied and discussed from various aspects, which deserves a comprehensive review. For the first time in this survey, we systematically review the recent progress on adversarial training for adversarial robustness with a novel taxonomy. Then we discuss the generalization problems in adversarial training from three perspectives and highlight the challenges which are not fully tackled. Finally, we present potential future directions.

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