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Portland State University

Academic institutionnorthamerica · us
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Research library109linked papers
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Selected work

Representative Papers

A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challenge

Oct 18, 2023arXiv.org

Managing and retrieving high-dimensional vector data poses significant challenges, particularly as traditional databases fail to meet performance requirements and the need for tight integration with large language models (LLMs) intensifies. Method: This paper systematically surveys four major approximate nearest neighbor search (ANNS) paradigms—hashing, tree-based indexing, graph-based methods (e.g., HNSW), and quantization (PQ/SQ)—and integrates hybrid optimization strategies. Contribution/Results: It introduces, for the first time, a “Four-Dimensional Methodology” framework tailored for industrial deployment of vector databases, analyzing trade-offs among accuracy, latency, memory footprint, and scalability. The work constructs a structured knowledge graph covering 200+ ANNS algorithms and proposes a novel paradigm for deep synergy between vector databases and LLMs. Collectively, these contributions provide both theoretical foundations and practical guidelines for system selection, architectural design, and development of AI-native database systems.

62 citations3 influentialRead paper

Dynamic and Adaptive Feature Generation with LLM

Jun 04, 2024arXiv.org

Existing feature engineering approaches suffer from three fundamental limitations: poor interpretability, weak generalizability, and inflexible strategies—hindering practical deployment across diverse scenarios. To address these challenges, this paper proposes the first large language model (LLM)-driven dynamic adaptive feature generation paradigm. Our method integrates task-aware prompting with semantic modeling of the feature space, enabling real-time, interpretable, and controllable feature generation tailored to both data characteristics and task requirements. It ensures cross-modal and cross-task generality while maintaining full transparency in the feature generation process. Extensive experiments on multiple structured and unstructured data tasks demonstrate that features generated by our approach improve feature quality by 23.6% and boost downstream model performance by an average of 11.4%, significantly outperforming conventional automated feature engineering methods.

21 citations1 influentialRead paper

Cross-Lingual Activation Steering for Multilingual Language Models

Jan 23, 2026

This work addresses the significant performance gap between high-resource and low-resource languages in multilingual large language models, which stems from an imbalance between shared and language-specific neuronal representations. The authors propose a training-free, inference-stage activation modulation method that selectively adjusts neuron activations to dynamically enhance separation among language clusters without altering model weights. Their approach demonstrates that effective cross-lingual transfer arises from functional differentiation rather than strict representational alignment, thereby unlocking the model’s latent multilingual capacity. Experimental results show consistent improvements across diverse tasks, with average gains of 2.3% in accuracy on classification and 3.4% in F1 score on generation tasks, while preserving performance on high-resource languages.

1 citationsRead paper

MTQ-Eval: Multilingual Text Quality Evaluation for Language Models

Nov 12, 2025

This work addresses the limited cross-lingual generalization capability of large language models (LLMs) in universal text quality assessment. To this end, we propose the first preference-learning-based multilingual text quality evaluation framework. It automatically generates high-quality preference data spanning 115 languages and trains open-source LLMs via supervised fine-tuning coupled with representation alignment—eliminating the need for human annotation. The framework significantly enhances the model’s ability to discriminate text quality across diverse languages and downstream tasks. Empirical results demonstrate substantial improvements over state-of-the-art methods on multilingual quality evaluation benchmarks. Moreover, it consistently boosts performance in key downstream applications, including machine translation and abstractive summarization. Our approach establishes a novel, scalable paradigm for universal multilingual text quality assessment.

1 citationsRead paper
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