Publications

* denotes equal contribution

2025

1. arXivPreprint

   Reflection-Window Decoding: Text Generation with Selective Refinement

   Zeyu Tang*, Zhenhao Chen*, Loka Li, Xiangchen Song, Yunlong Deng, Yifan Shen, Guangyi Chen, Peter Spirtes, and Kun Zhang

   arXiv Preprint, 2025.

   ABSTRACT arXiv PDF

   The autoregressive decoding for text generation in large language models (LLMs), while widely used, is inherently suboptimal due to the lack of a built-in mechanism to perform refinement and/or correction of the generated content. In this paper, we consider optimality in terms of the joint probability over the generated response, when jointly considering all tokens at the same time. We theoretically characterize the potential deviation of the autoregressively generated response from its globally optimal counterpart that is of the same length. Our analysis suggests that we need to be cautious when noticeable uncertainty arises during text generation, which may signal the sub-optimality of the generation history. To address the pitfall of autoregressive decoding for text generation, we propose an approach that incorporates a sliding reflection window and a pausing criterion, such that refinement and generation can be carried out interchangeably as the decoding proceeds. Our selective refinement framework strikes a balance between efficiency and optimality, and our extensive experimental results demonstrate the effectiveness of our approach.

2. ICLR

   Prompting Fairness: Integrating Causality to Debias Large Language Models

   Jingling Li*, Zeyu Tang*, Xiaoyu Liu, Peter Spirtes, Kun Zhang, Liu Leqi, and Yang Liu

   In Proceedings of the 13th International Conference on Learning Representations (preliminary version titled "Steering LLMs Towards Unbiased Responses: A Causality-Guided Debiasing Framework"), 2025.

   ABSTRACT arXiv PDF

   Large language models (LLMs), despite their remarkable capabilities, are susceptible to generating biased and discriminatory responses. As LLMs increasingly influence high-stakes decision-making (e.g., hiring and healthcare), mitigating these biases becomes critical. In this work, we propose a causality-guided debiasing framework to tackle social biases, aiming to reduce the objectionable dependence between LLMs’ decisions and the social information in the input. Our framework introduces a novel perspective to identify how social information can affect an LLM’s decision through different causal pathways. Leveraging these causal insights, we outline principled prompting strategies that regulate these pathways through selection mechanisms. This framework not only unifies existing prompting-based debiasing techniques, but also opens up new directions for reducing bias by encouraging the model to prioritize fact-based reasoning over reliance on biased social cues. We validate our framework through extensive experiments on real-world datasets across multiple domains, demonstrating its effectiveness in debiasing LLM decisions, even with only black-box access to the model.

3. ICLROral

   When Selection Meets Intervention: Additional Complexities in Causal Discovery

   Haoyue Dai, Ignavier Ng, Jianle Sun, Zeyu Tang, Gongxu Luo, Xinshuai Dong, Peter Spirtes, and Kun Zhang

   In Proceedings of the 13th International Conference on Learning Representations, 2025.

   ABSTRACT arXiv PDF

   We address the common yet often-overlooked selection bias in interventional studies, where subjects are selectively enrolled into experiments. For instance, participants in a drug trial are usually patients of the relevant disease; A/B tests on mobile applications target existing users only, and gene perturbation studies typically focus on specific cell types, such as cancer cells. Ignoring this bias leads to incorrect causal discovery results. Even when recognized, the existing paradigm for interventional causal discovery still fails to address it. This is because subtle differences in when and where interventions happen can lead to significantly different statistical patterns. We capture this dynamic by introducing a graphical model that explicitly accounts for both the observed world (where interventions are applied) and the counterfactual world (where selection occurs while interventions have not been applied). We characterize the Markov property of the model, and propose a provably sound algorithm to identify causal relations as well as selection mechanisms up to the equivalence class, from data with soft interventions and unknown targets. Through synthetic and real-world experiments, we demonstrate that our algorithm effectively identifies true causal relations despite the presence of selection bias.

4. CVPR

   SmartCLIP: Modular Vision-language Alignment with Identification Guarantees

   Shaoan Xie*, Lingjing Kong*, Yujia Zheng, Yu Yao, Zeyu Tang, Eric P. Xing, Guangyi Chen, and Kun Zhang

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025.

   ABSTRACT

   Contrastive Language-Image Pre-training (CLIP) has emerged as a pivotal model in computer vision and multimodal learning, achieving state-of-the-art performance at aligning visual and textual representations through contrastive learning. However, CLIP struggles with potential information misalignment in many image-text datasets and suffers from entangled representation. On the one hand, short captions for a single image in datasets like MSCOCO may describe disjoint regions in the image, leaving the model uncertain about which visual features to retain or disregard. On the other hand, directly aligning long captions with images can lead to the retention of entangled details, preventing the model from learning disentangled, atomic concepts – ultimately limiting its generalization on certain downstream tasks involving short prompts. In this paper, we establish theoretical conditions that enable flexible alignment between textual and visual representations across varying levels of granularity. Specifically, our framework ensures that a model can not only \emphpreserve cross-modal semantic information in its entirety but also \emphdisentangle visual representations to capture fine-grained textual concepts. Building on this foundation, we introduce \ours, a novel approach that identifies and aligns the most relevant visual and textual representations in a modular manner. Superior performance across various tasks demonstrates its capability to handle information misalignment and supports our identification theory.

2024

1. arXivPreprint

   Steering LLMs Towards Unbiased Responses: A Causality-Guided Debiasing Framework

   Jingling Li*, Zeyu Tang*, Xiaoyu Liu, Peter Spirtes, Kun Zhang, Liu Leqi, and Yang Liu

   arXiv Preprint, 2024.

   ABSTRACT arXiv PDF

   Large language models (LLMs) can easily generate biased and discriminative responses. As LLMs tap into consequential decision-making (e.g., hiring and healthcare), it is of crucial importance to develop strategies to mitigate these biases. This paper focuses on social bias, tackling the association between demographic information and LLM outputs. We propose a causality-guided debiasing framework that utilizes causal understandings of (1) the data-generating process of the training corpus fed to LLMs, and (2) the internal reasoning process of LLM inference, to guide the design of prompts for debiasing LLM outputs through selection mechanisms. Our framework unifies existing de-biasing prompting approaches such as inhibitive instructions and in-context contrastive examples, and sheds light on new ways of debiasing by encouraging bias-free reasoning. Our strong empirical performance on real-world datasets demonstrates that our framework provides principled guidelines on debiasing LLM outputs even with only the black-box access.

2. arXivPreprint

   Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges

   Usman Gohar, Zeyu Tang, Jialu Wang, Kun Zhang, Peter Spirtes, Yang Liu, and Lu Cheng

   arXiv Preprint, 2024.

   ABSTRACT arXiv PDF

   The widespread integration of Machine Learning systems in daily life, particularly in high-stakes domains, has raised concerns about the fairness implications. While prior works have investigated static fairness measures, recent studies reveal that automated decision-making has long-term implications and that off-the-shelf fairness approaches may not serve the purpose of achieving long-term fairness. Additionally, the existence of feedback loops and the interaction between models and the environment introduces additional complexities that may deviate from the initial fairness goals. In this survey, we review existing literature on long-term fairness from different perspectives and present a taxonomy for long-term fairness studies. We highlight key challenges and consider future research directions, analyzing both current issues and potential further explorations.

3. ICML

   Detecting and Identifying Selection Structure in Sequential Data

   Yujia Zheng, Zeyu Tang, Yiwen Qiu, Bernhard Schölkopf, and Kun Zhang

   In Proceedings of the 41st International Conference on Machine Learning, 2024.

   ABSTRACT arXiv PDF

   We argue that the selective inclusion of data points based on latent objectives is common in practical situations, such as music sequences. Since this selection process often distorts statistical analysis, previous work primarily views it as a bias to be corrected and proposes various methods to mitigate its effect. However, while controlling this bias is crucial, selection also offers an opportunity to provide a deeper insight into the hidden generation process, as it is a fundamental mechanism underlying what we observe. In particular, overlooking selection in sequential data can lead to an incomplete or overcomplicated inductive bias in modeling, such as assuming a universal autoregressive structure for all dependencies. Therefore, rather than merely viewing it as a bias, we explore the causal structure of selection in sequential data to delve deeper into the complete causal process. Specifically, we show that selection structure is identifiable without any parametric assumptions or interventional experiments. Moreover, even in cases where selection variables coexist with latent confounders, we still establish the nonparametric identifiability under appropriate structural conditions. Meanwhile, we also propose a provably correct algorithm to detect and identify selection structures as well as other types of dependencies. The framework has been validated empirically on both synthetic data and real-world music.

4. ICLRSpotlight

   Procedural Fairness Through Decoupling Objectionable Data Generating Components

   Zeyu Tang, Jialu Wang, Yang Liu, Peter Spirtes, and Kun Zhang

   In Proceedings of the 12th International Conference on Learning Representations (preliminary version presented in NeurIPS 2023 AFT workshop), 2024.

   ABSTRACT arXiv PDF

   We reveal and address the frequently overlooked yet important issue of disguised procedural unfairness, namely, the potentially inadvertent alterations on the behavior of neutral (i.e., not problematic) aspects of data generating process, and/or the lack of procedural assurance of the greatest benefit of the least advantaged individuals. Inspired by John Rawls’s advocacy for pure procedural justice (Rawls, 1971; 2001), we view automated decision-making as a microcosm of social institutions, and consider how the data generating process itself can satisfy the requirements of procedural fairness. We propose a framework that decouples the objectionable data generating components from the neutral ones by utilizing reference points and the associated value instantiation rule. Our findings highlight the necessity of preventing disguised procedural unfairness, drawing attention not only to the objectionable data generating components that we aim to mitigate, but also more importantly, to the neutral components that we intend to keep unaffected.

2023

1. ACM CSUR

   What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective

   Zeyu Tang, Jiji Zhang, and Kun Zhang

   ACM Computing Surveys, 2023.

   ABSTRACT arXiv PDF

   Algorithmic fairness has attracted increasing attention in the machine learning community. Various definitions are proposed in the literature, but the differences and connections among them are not clearly addressed. In this paper, we review and reflect on various fairness notions previously proposed in machine learning literature, and make an attempt to draw connections to arguments in moral and political philosophy, especially theories of justice. We also consider fairness inquiries from a dynamic perspective, and further consider the long-term impact that is induced by current prediction and decision. In light of the differences in the characterized fairness, we present a flowchart that encompasses implicit assumptions and expected outcomes of different types of fairness inquiries on the data generating process, on the predicted outcome, and on the induced impact, respectively. This paper demonstrates the importance of matching the mission (which kind of fairness one would like to enforce) and the means (which spectrum of fairness analysis is of interest, what is the appropriate analyzing scheme) to fulfill the intended purpose.

2. ICMLAward

   Model Transferability With Responsive Decision Subjects

   Yatong Chen, Zeyu Tang, Kun Zhang, and Yang Liu

   In Proceedings of the 40th International Conference on Machine Learning (preliminary version won the BEST PAPER AWARD in ICML 2022 AdvML workshop), 2023.

   ABSTRACT arXiv PDF

   This paper studies model transferability when human decision subjects respond to a deployed machine learning model. In our setting, an agent or a user corresponds to a sample (X,Y) drawn from a distribution D and will face a model h and its classification result h(X). Agents can modify X to adapt to h, which will incur a distribution shift on (X,Y). Therefore, when training h, the learner will need to consider the subsequently "induced" distribution when the output model is deployed. Our formulation is motivated by applications where the deployed machine learning models interact with human agents, and will ultimately face responsive and interactive data distributions. We formalize the discussions of the transferability of a model by studying how the model trained on the available source distribution (data) would translate to the performance on the induced domain. We provide both upper bounds for the performance gap due to the induced domain shift, as well as lower bound for the trade-offs that a classifier has to suffer on either the source training distribution or the induced target distribution. We provide further instantiated analysis for two popular domain adaptation settings with covariate shift and target shift.

3. ICLR

   Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors

   Zeyu Tang, Yatong Chen, Yang Liu, and Kun Zhang

   In Proceedings of the 11th International Conference on Learning Representations (preliminary version presented in NeurIPS 2022 AFCP workshop), 2023.

   ABSTRACT arXiv PDF

   The pursuit of long-term fairness involves the interplay between decision-making and the underlying data generating process. In this paper, through causal modeling with a directed acyclic graph (DAG) on the decision-distribution interplay, we investigate the possibility of achieving long-term fairness from a dynamic perspective. We propose Tier Balancing, a technically more challenging but more natural notion to achieve in the context of long-term, dynamic fairness analysis. Different from previous fairness notions that are defined purely on observed variables, our notion goes one step further, capturing behind-the-scenes situation changes on the unobserved latent causal factors that directly carry out the influence from the current decision to the future data distribution. Under the specified dynamics, we prove that in general one cannot achieve the long-term fairness goal only through one-step interventions. Furthermore, in the effort of approaching long-term fairness, we consider the mission of "getting closer to" the long-term fairness goal and present possibility and impossibility results accordingly.

2022

1. CLeaRSpotlight

   Attainability and Optimality: The Equalized Odds Fairness Revisited

   Zeyu Tang, and Kun Zhang

   In Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022.

   ABSTRACT arXiv PDF

   Fairness of machine learning algorithms has been of increasing interest. In order to suppress or eliminate discrimination in prediction, various notions as well as approaches have been proposed to impose fairness. Given a notion of fairness, an essential problem is then whether or not it can always be attained, even if with an unlimited amount of data. This issue is, however, not well addressed yet. In this paper, focusing on the Equalized Odds notion of fairness, we consider the attainability of this criterion and, furthermore, if it is attainable, the optimality of the prediction performance under various settings. In particular, for prediction performed by a deterministic function of input features, we give conditions under which Equalized Odds can hold true; if the stochastic prediction is acceptable, we show that under mild assumptions, fair predictors can always be derived. For classification, we further prove that compared to enforcing fairness by post-processing, one can always benefit from exploiting all available features during training and get potentially better prediction performance while remaining fair. Moreover, while stochastic prediction can attain Equalized Odds with theoretical guarantees, we also discuss its limitation and potential negative social impacts.