Xujiang Zhao NEC Labs America

Xujiang Zhao is a researcher in the Data Science & System Security department at NEC Laboratories America, based in Princeton, New Jersey. He holds a B.S. in Civil Engineering from Chongqing University and an M.S. in Computer Science from the University of Science and Technology of China. He earned his PhD in Computer Science from the University of Texas at Dallas, and his academic training provided a strong foundation in both theoretical and applied aspects of computing, which continues to shape his contributions at NEC.

At NEC Labs, Zhao’s research focuses on aligning large language models (LLMs) with human intent through techniques that enhance explainability, factual consistency, uncertainty estimation, and robustness. He develops methods that make LLMs more transparent and reliable, ensuring that they can be applied in sensitive, high-stakes environments. A key area of his work is building collaborative agent systems that integrate LLMs with domain-specific expertise and human feedback loops, enabling AI to work more effectively as a partner in decision-making.

Beyond language alignment, Zhao explores applications in image–text retrieval, synthetic media detection, and multi-agent reasoning, areas that are increasingly critical for enterprise knowledge management, misinformation defense, and the verification of AI-generated content. By combining fundamental advances in machine learning with applied research, his work pushes forward the responsible and practical use of foundation models across industries.

Posts

SFS: Smarter Code Space Search improves LLM Inference Scaling

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We frame code generation as a black-box optimization problem within the code space and demonstrate how optimization-inspired techniques can enhance inference scaling. Based on this perspective, we propose SCATTERED FOREST SEARCH (SFS), a novel approach that improves solution diversity and better exploits feedback during evolutionary search. Our theoretical analysis illustrates how these methods help avoid local optima during optimization, leading to more efficient exploration. Extensive experiments on HumanEval, MBPP, APPS, CodeContests, and Leetcode reveal significant performance gains. For instance, our method achieves a pass@1 rate of 67.1% on HumanEval+ and 87.2% on HumanEval with GPT-3.5, marking improvements of 8.6% and 4.3% over the state-of-the-art, while also halving the iterations needed to find the correct solution. Furthermore, our approach scales more efficiently than existing search techniques, including tree search, line search, and repeated sampling.

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Large Language Models Can Be Contextual Privacy Protection Learners

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The proliferation of Large Language Models (LLMs) has driven considerable interest in fine-tuning them with domain-specific data to create specialized language models. Nevertheless, such domain-specific fine-tuning data often contains contextually sensitive personally identifiable information (PII). Direct fine-tuning LLMs on this data without privacy protection poses a risk of data leakage of sensitive PII during inference time. To address this challenge, we introduce Contextual Privacy Protection Language Models (CPPLM), a novel paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding inference-time data privacy. Our work offers a theoretical analysis for model design and delves into various techniques such as corpus curation, penalty-based unlikelihood in training loss, and instruction-based tuning, etc. Extensive experiments across diverse datasets and scenarios demonstrate the effectiveness of our approaches. In particular, instruction tuning with both positive and negative examples, stands out as a promising method, effectively protecting private data while enhancing the model s knowledge. Our work underscores the potential for Large Language Models as robust contextual privacy protection learners.

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Towards Counterfactual Fairness-aware Domain Generalization in Changing Environments

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Recognizing domain generalization as a commonplace challenge in machine learning, data distribution might progressively evolve across a continuum of sequential domains in practical scenarios. While current methodologies primarily concentrate on bolstering model effectiveness within these new domains, they tend to neglect issues of fairness throughout the learning process. In response, we propose an innovative framework known as Disentanglement for Counterfactual Fairness-aware Domain Generalization (DCFDG). This approach adeptly removes domain-specific information and sensitive information from the embedded representation of classification features. To scrutinize the intricate interplay between semantic information, domain-specific information, and sensitive attributes, we systematically partition the exogenous factors into four latent variables. By incorporating fairness regularization, we utilize semantic information exclusively for classification purposes. Empirical validation on synthetic and authentic datasets substantiates the efficacy of our approach, demonstrating elevated accuracy levels while ensuring the preservation of fairness amidst the evolving landscape of continuous domains.

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RIO-CPD: A Riemannian Geometric Method for Correlation-aware Online Change Point Detection

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The objective of change point detection is to identify abrupt changes at potentially multiple points within a data sequence. This task is particularly challenging in the online setting where various types of changes can occur, including shifts in both the marginal and joint distributions of the data. This paper tackles these challenges by sequentially tracking correlation matrices on their Riemannian geometry, where the geodesic distances accurately capture the development of correlations. We propose Rio-CPD, a non-parametric correlation-aware online change point detection framework that combines the Riemannian geometry of the manifold of symmetric positive definite matrices and the cumulative sum statistic (CUSUM) for detecting change points. Rio-CPD enhances CUSUM by computing the geodesic distance from present observations to the Frechet mean of previous observations. With careful choice of metrics equipped to the Riemannian geometry, Rio-CPD is simple and computationally efficient. Experimental results on both synthetic and real-world datasets demonstrate that Rio-CPD outperforms existing methods in detection accuracy and efficiency.

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Knowledge-enhanced Prompt Learning for Open-domain Commonsense Reasoning

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Neural language models for commonsense reasoning often formulate the problem as a QA task and make predictions based on learned representations of language after fine-tuning. However, without providing any fine-tuning data and pre-defined answer candidates, can neural language models still answer commonsense reasoning questions only relying on external knowledge? In this work, we investigate a unique yet challenging problem-open-domain commonsense reasoning that aims to answer questions without providing any answer candidates and fine-tuning examples. A team comprising NECLA (NEC Laboratories America) and NEC Digital Business Platform Unit proposed method leverages neural language models to iteratively retrieve reasoning chains on the external knowledge base, which does not require task-specific supervision. The reasoning chains can help to identify the most precise answer to the commonsense question and its corresponding knowledge statements to justify the answer choice. This technology has proven its effectiveness in a diverse array of business domains.

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Uncertainty Quantification for In-Context Learning of Large Language Models

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In-context learning has emerged as a groundbreaking ability of Large Language Models (LLMs) and revolutionized various fields by providing a few task-relevant demonstrations in the prompt. However, trustworthy issues with LLM’s response, such as hallucination, have also been actively discussed. Existing works have been devoted to quantifying the uncertainty in LLM’s response, but they often overlook the complex nature of LLMs and the uniqueness of in-context learning. In this work, we delve into the predictive uncertainty of LLMs associated with in-context learning, highlighting that such uncertainties may stem from both the provided demonstrations (aleatoric uncertainty) and ambiguities tied to the model’s configurations (epistemic uncertainty). We propose a novel formulation and corresponding estimation method to quantify both types of uncertainties. The proposed method offers an unsupervised way to understand the prediction of in-context learning in a plug-and-play fashion. Extensive experiments are conducted to demonstrate the effectiveness of the decomposition. The code and data are available at: https://github.com/lingchen0331/UQ_ICL.

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Pruning as a Domain-specific LLM Extractor

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Large Language Models (LLMs) have exhibited remarkable proficiency across a wide array of NLP tasks. However, the escalation in model size also engenders substantial deployment costs. While few efforts have explored model pruning techniques to reduce the size of LLMs, they mainly center on general or task-specific weights. This leads to suboptimal performance due to lacking specificity on the target domain or generality on different tasks when applied to domain-specific challenges. This work introduces an innovative unstructured dual-pruning methodology, D-PRUNER, for domain-specific compression on LLM. It extracts a compressed, domain-specific, and task agnostic LLM by identifying LLM weights that are pivotal for general capabilities, like linguistic capability and multi-task solving, and domain-specific knowledge. More specifically, we first assess general weight importance by quantifying the error incurred upon their removal with the help of an open-domain calibration dataset. Then, we utilize this general weight importance to refine the training loss, so that it preserves generality when fitting into a specific domain. Moreover, by efficiently approximating weight importance with the refined training loss on a domain-specific calibration dataset, we obtain a pruned model emphasizing generality and specificity. Our comprehensive experiments across various tasks in healthcare and legal domains show the effectiveness of D-PRUNER in domain-specific compression. Our code is available at https: //github.com/psunlpgroup/D-Pruner.

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Advancing Sustainability in Global Supply Chains through Agent-based Simulation

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In today’s world, with its complex global supply chains, the difficulties and uncertainties we face offer both challenges and opportunities for making things better, especially in terms of efficiency and sustainability. These challenges grow due to unpredictable events, such as natural disasters, unexpected incidents, and unusual business practices, pushing us towards more advanced modeling methods that focus on reducing risks and enhancing sustainability. In this paper, we present a new agent-based simulation approach that goes beyond the usual limits of supply chain simulations by incorporating sustainability directly into supply chain operations using reinforcement learning (RL) algorithms. We introduce MOGI, a sustainable supply chain simulation system that takes carbon emissions into account in its main operations. Additionally, we examine how effective a multi-agent RL strategy is in dealing with the complex and uncertain nature of supply chains that span multiple levels. By comparing this strategy with traditional heuristic methods, our study looks at how well single versus multiple RL agents can manage risks and improve sustainability in both the beginning and end parts of the supply chain. The results of our experiments show that strategies based on RL are much better than traditional methods at managing risks, making profits, and achieving sustainability goals.

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Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty

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Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text, typically in the form of (subject, relation, object) triples. Despite the potential of large language models (LLMs) like ChatGPT as a general task solver, they lag behind state-of-the-art (supervised) methods in OIE tasks due to two key issues. First, LLMs struggle to distinguish irrelevant context from relevant relations and generate structured output due to the restrictions on fine-tuning the model. Second, LLMs generate responses autoregressively based on probability, which makes the predicted relations lack confidence. In this paper, we assess the capabilities of LLMs in improving the OIE task. Particularly, we propose various in-context learning strategies to enhance LLM’s instruction-following ability and a demonstration uncertainty quantification module to enhance the confidence of the generated relations. Our experiments on three OIE benchmark datasets show that our approach holds its own against established supervised methods, both quantitatively and qualitatively.

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Open-Ended Commonsense Reasoning with Unrestricted Answer Scope

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Open-ended Commonsense Reasoning is defined as solving a commonsense question without providing 1) a short list of answer candidates and 2) a pre-defined answer scope. Conventional ways of formulating the commonsense question into a question-answering form or utilizing external knowledge to learn retrieval-based methods are less applicable in the open-ended setting due to an inherent challenge. Without pre-defining an answer scope or a few candidates, open-ended commonsense reasoning entails predicting answers by searching over an extremely large searching space. Moreover, most questions require implicit multi-hop reasoning, which presents even more challenges to our problem. In this work, we leverage pre-trained language models to iteratively retrieve reasoning paths on the external knowledge base, which does not require task-specific supervision. The reasoning paths can help to identify the most precise answer to the commonsense question. We conduct experiments on two commonsense benchmark datasets. Compared to other approaches, our proposed method achieves better performance both quantitatively and qualitatively.

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