arXiv subjects

Kai Guo

Publications and source records attributed to Kai Guo.

At least 55 records · Page 3Linked to original sources

arXiv2025

A vibe coding learning design to enhance EFL students' talking to, through, and about AI

This innovative practice article reports on the piloting of vibe coding (using natural language to create software applications with AI) for English as a Foreign Language (EFL) education. We developed a human-AI meta-languaging framework with three dimensions: talking to AI (prompt engineering), talking through AI (negotiating authorship), and talking about AI (mental models of AI). Using backward design principles, we created a four-hour workshop where two students designed applications addressing authentic EFL writing challenges. We adopted a case study methodology, collecting data from worksheets and video recordings, think-aloud protocols, screen recordings, and AI-generated images. Contrasting cases showed one student successfully vibe coding a functional application cohering to her intended design, while another encountered technical difficulties with major gaps between intended design and actual functionality. Analysis reveals differences in students' prompt engineering approaches, suggesting different AI mental models and tensions in attributing authorship. We argue that AI functions as a beneficial languaging machine, and that differences in how students talk to, through, and about AI explain vibe coding outcome variations. Findings indicate that effective vibe coding instruction requires explicit meta-languaging scaffolding, teaching structured prompt engineering, facilitating critical authorship discussions, and developing vocabulary for articulating AI mental models.

cs.CY↗

arXiv2025

Product vs. Process: Exploring EFL Students' Editing of AI-Generated Text for Expository Writing

Text generated by artificial intelligence (AI) chatbots is increasingly used in English as a foreign language (EFL) writing contexts, yet its impact on students' expository writing process and compositions remains understudied. This research examines how EFL secondary students edit AI-generated text. Exploring editing behaviors in their expository writing process and in expository compositions, and their effect on human-rated scores for content, organization, language, and overall quality. Participants were 39 Hong Kong secondary students who wrote an expository composition with AI chatbots in a workshop. A convergent design was employed to analyze their screen recordings and compositions to examine students' editing behaviors and writing qualities. Analytical methods included qualitative coding, descriptive statistics, temporal sequence analysis, human-rated scoring, and multiple linear regression analysis. We analyzed over 260 edits per dataset, and identified two editing patterns: one where students refined introductory units repeatedly before progressing, and another where they quickly shifted to extensive edits in body units (e.g., topic and supporting sentences). MLR analyses revealed that the number of AI-generated words positively predicted all score dimensions, while most editing variables showed minimal impact. These results suggest a disconnect between students' significant editing effort and improved composition quality, indicating AI supports but does not replace writing skills. The findings highlight the importance of genre-specific instruction and process-focused writing before AI integration. Educators should also develop assessments valuing both process and product to encourage critical engagement with AI text.

cs.CL↗

arXiv2025

Exploring EFL Secondary Students' AI-generated Text Editing While Composition Writing

Generative Artificial Intelligence is transforming how English as a foreign language students write. Still, little is known about how students manipulate text generated by generative AI during the writing process. This study investigates how EFL secondary school students integrate and modify AI-generated text when completing an expository writing task. The study employed an exploratory mixed-methods design. Screen recordings were collected from 29 Hong Kong secondary school students who attended an AI-assisted writing workshop and recorded their screens while using generative AI to write an article. Content analysis with hierarchical coding and thematic analysis with a multiple case study approach were adopted to analyze the recordings. 15 types of AI-generated text edits across seven categories were identified from the recordings. Notably, AI-initiated edits from iOS and Google Docs emerged as unanticipated sources of AI-generated text. A thematic analysis revealed four patterns of students' editing behaviors based on planning and drafting direction: planning with top-down drafting and revising; top-down drafting and revising without planning; planning with bottom-up drafting and revising; and bottom-up drafting and revising without planning. Network graphs illustrate cases of each pattern, demonstrating that students' interactions with AI-generated text involve more complex cognitive processes than simple text insertion. The findings challenge assumptions about students' passive, simplistic use of generative AI tools and have implications for developing explicit instructional approaches to teaching AI-generated text editing strategies in the AFL writing pedagogy.

cs.CY↗

arXiv2025

Empowering GraphRAG with Knowledge Filtering and Integration

In recent years, large language models (LLMs) have revolutionized the field of natural language processing. However, they often suffer from knowledge gaps and hallucinations. Graph retrieval-augmented generation (GraphRAG) enhances LLM reasoning by integrating structured knowledge from external graphs. However, we identify two key challenges that plague GraphRAG:(1) Retrieving noisy and irrelevant information can degrade performance and (2)Excessive reliance on external knowledge suppresses the model's intrinsic reasoning. To address these issues, we propose GraphRAG-FI (Filtering and Integration), consisting of GraphRAG-Filtering and GraphRAG-Integration. GraphRAG-Filtering employs a two-stage filtering mechanism to refine retrieved information. GraphRAG-Integration employs a logits-based selection strategy to balance external knowledge from GraphRAG with the LLM's intrinsic reasoning,reducing over-reliance on retrievals. Experiments on knowledge graph QA tasks demonstrate that GraphRAG-FI significantly improves reasoning performance across multiple backbone models, establishing a more reliable and effective GraphRAG framework.

cs.AI↗

arXiv2025

Approaching the Limits to EFL Writing Enhancement with AI-generated Text and Diverse Learners

Generative artificial intelligence (AI) chatbots, such as ChatGPT, are reshaping how English as a foreign language (EFL) students write since students can compose texts by integrating their own words with AI-generated text. This study investigated how 59 Hong Kong secondary school students with varying levels of academic achievement interacted with AI-generated text to compose a feature article, exploring whether any interaction patterns benefited the overall quality of the article. Through content analysis, multiple linear regression and cluster analysis, we found the overall number of words -- whether AI- or human-generated -- is the main predictor of writing quality. However, the impact varies by students' competence to write independently, for instance, by using their own words accurately and coherently to compose a text, and to follow specific interaction patterns with AI-generated text. Therefore, although composing texts with human words and AI-generated text may become prevalent in EFL writing classrooms, without educators' careful attention to EFL writing pedagogy and AI literacy, high-achieving students stand to benefit more from using AI-generated text than low-achieving students.

cs.CL↗

arXiv2025

Aligning Large Language Models and Geometric Deep Models for Protein Representation

Latent representation alignment has become a foundational technique for constructing multimodal large language models (MLLM) by mapping embeddings from different modalities into a shared space, often aligned with the embedding space of large language models (LLMs) to enable effective cross-modal understanding. While preliminary protein-focused MLLMs have emerged, they have predominantly relied on heuristic approaches, lacking a fundamental understanding of optimal alignment practices across representations. In this study, we explore the alignment of multimodal representations between LLMs and Geometric Deep Models (GDMs) in the protein domain. We comprehensively evaluate three state-of-the-art LLMs (Gemma2-2B, LLaMa3.1-8B, and LLaMa3.1-70B) with four protein-specialized GDMs (GearNet, GVP, ScanNet, GAT). Our work examines alignment factors from both model and protein perspectives, identifying challenges in current alignment methodologies and proposing strategies to improve the alignment process. Our key findings reveal that GDMs incorporating both graph and 3D structural information align better with LLMs, larger LLMs demonstrate improved alignment capabilities, and protein rarity significantly impacts alignment performance. We also find that increasing GDM embedding dimensions, using two-layer projection heads, and fine-tuning LLMs on protein-specific data substantially enhance alignment quality. These strategies offer potential enhancements to the performance of protein-related multimodal models. Our code and data are available at https://github.com/Tizzzzy/LLM-GDM-alignment.

cs.LG↗

arXiv2025

Optimized quantum entanglement network enabled by a state-multiplexing quantum light source

A fully connected quantum network with a wavelength division multiplexing architecture plays an increasingly pivotal role in quantum information technology. With such architecture, an entanglement-based network has been demonstrated in which an entangled photon-pair source distributes quantum entanglement resources to many users. Despite these remarkable advances, the scalability of the architecture could be constrained by the finite spectrum resource, where O(N^2)wavelength channels are needed to connect N users, thus impeding further progress in real-world scenarios. Here, we propose an optimized scheme for the wavelength division multiplexing entanglement-based network using a state-multiplexing quantum light source. With a dual-pump configuration, the feasibility of our approach is demonstrated by generating state-multiplexing photon pairs at multiple wavelength channels with a silicon nitride microring resonator chip. In our demonstration, we establish a fully connected graph between four users with six wavelength channels - saving half of which without sacrificing functionality and performance of the secure communication. A total asymptotic secure key rate of 1946.9 bps is obtained by performing the BBM92 protocol with the distributed state. The network topology of our method has great potential for developing a scalable quantum network with significantly minimized infrastructure requirements.

quant-ph↗

arXiv2025

Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approach

Large Language Models (LLMs) enhanced with external contexts, such as through retrieval-augmented generation (RAG), often face challenges in handling imperfect evidence. They tend to over-rely on external knowledge, making them vulnerable to misleading and unhelpful contexts. To address this, we propose the concept of context-robust LLMs, which can effectively balance internal knowledge with external context, similar to human cognitive processes. Specifically, context-robust LLMs should rely on external context only when lacking internal knowledge, identify contradictions between internal and external knowledge, and disregard unhelpful contexts. To achieve this goal, we introduce Grft, a lightweight and plug-and-play gated representation fine-tuning approach. Grft consists of two key components: a gating mechanism to detect and filter problematic inputs, and low-rank representation adapters to adjust hidden representations. By training a lightweight intervention function with only 0.0004\% of model size on fewer than 200 examples, Grft can effectively adapt LLMs towards context-robust behaviors.

cs.CL↗

arXiv2025

Retrieval-Augmented Generation with Graphs (GraphRAG)

Retrieval-augmented generation (RAG) is a powerful technique that enhances downstream task execution by retrieving additional information, such as knowledge, skills, and tools from external sources. Graph, by its intrinsic "nodes connected by edges" nature, encodes massive heterogeneous and relational information, making it a golden resource for RAG in tremendous real-world applications. As a result, we have recently witnessed increasing attention on equipping RAG with Graph, i.e., GraphRAG. However, unlike conventional RAG, where the retriever, generator, and external data sources can be uniformly designed in the neural-embedding space, the uniqueness of graph-structured data, such as diverse-formatted and domain-specific relational knowledge, poses unique and significant challenges when designing GraphRAG for different domains. Given the broad applicability, the associated design challenges, and the recent surge in GraphRAG, a systematic and up-to-date survey of its key concepts and techniques is urgently desired. Following this motivation, we present a comprehensive and up-to-date survey on GraphRAG. Our survey first proposes a holistic GraphRAG framework by defining its key components, including query processor, retriever, organizer, generator, and data source. Furthermore, recognizing that graphs in different domains exhibit distinct relational patterns and require dedicated designs, we review GraphRAG techniques uniquely tailored to each domain. Finally, we discuss research challenges and brainstorm directions to inspire cross-disciplinary opportunities. Our survey repository is publicly maintained at https://github.com/Graph-RAG/GraphRAG/.

cs.IR↗

arXiv2024

Acoustic Vortex Filter Based on Tunable Metasurfaces

In this paper, we present an acoustic vortex filter (AVF) based on tunable metasurfaces, which can selectively filter the incident multiplexed vortices that carry different orbital angular momentum (OAM). Our metasurface-based AVF is composed of an upper acoustic metasurface (UAM) and a lower acoustic metasurface (LAM), of which the intrinsic topological charge (ITC) can be tuned by mechanically rotating the UAM along its central axis. Due to the critical order of the propagating vortex modes in waveguide, controlling the ITC of the AVF allows for the selective filtering of incoming multiplexed acoustic vortex beams based on the sound vortex diffraction in phase-gradient metasurface, which endows the vortex filter the capability that let the incident vortex of specific OAM pass through it. In the following demonstration, both in theory and experiment, we design the AVF and effectively filter the acoustic vortices with two opposite topological charges (TCs) by simply altering the orientation angle of the UAM. Based on this, we further demonstrate its application in asymmetric acoustic wave transmission. Our work offers an approach to selectively filter the incident acoustic vortex, which improves the capability to control the acoustic OAM via metasurfaces.

physics.app-ph↗

arXiv2024

Continuous-wave amplitude control via the interference phenomenon in acoustic structures

We propose a strategy to continuously tune the amplitude of acoustic waves based on the interference among two mode-conversion paths in passive acoustic structures. The interference phenomenon is attributed to two conjugate acoustic geometric phases obtained with two mode-conversion processes in hybrid-type geometric-phase meta-atom (HGPM) pair. Notably, 100% modulation depth of the wave amplitude is achievable by simply varying the local orientation angle of meta-atom. We utilize the acoustic structure made of two cylindrical resonators to construct deep-subwavelength secondary source with designated initial phase delay, and HGPM supporting desired mode-conversion functionality is accordingly fabricated with four secondary sources. Both theory and experiment consistently verify the continuous amplitude modulation function of HGPM pair, which showcases a general scheme for reconfigurable amplitude-type acoustic meta-devices, i.e., those that require grayscale amplitude modulation for acoustic field engineering.

physics.app-ph↗

arXiv2024

ASD-Diffusion: Anomalous Sound Detection with Diffusion Models

Unsupervised Anomalous Sound Detection (ASD) aims to design a generalizable method that can be used to detect anomalies when only normal sounds are given. In this paper, Anomalous Sound Detection based on Diffusion Models (ASD-Diffusion) is proposed for ASD in real-world factories. In our pipeline, the anomalies in acoustic features are reconstructed from their noisy corrupted features into their approximate normal pattern. Secondly, a post-processing anomalies filter algorithm is proposed to detect anomalies that exhibit significant deviation from the original input after reconstruction. Furthermore, denoising diffusion implicit model is introduced to accelerate the inference speed by a longer sampling interval of the denoising process. The proposed method is innovative in the application of diffusion models as a new scheme. Experimental results on the development set of DCASE 2023 challenge task 2 outperform the baseline by 7.75%, demonstrating the effectiveness of the proposed method.

cs.SD↗

arXiv2024

A Practical Gated Recurrent Transformer Network Incorporating Multiple Fusions for Video Denoising

State-of-the-art (SOTA) video denoising methods employ multi-frame simultaneous denoising mechanisms, resulting in significant delays (e.g., 16 frames), making them impractical for real-time cameras. To overcome this limitation, we propose a multi-fusion gated recurrent Transformer network (GRTN) that achieves SOTA denoising performance with only a single-frame delay. Specifically, the spatial denoising module extracts features from the current frame, while the reset gate selects relevant information from the previous frame and fuses it with current frame features via the temporal denoising module. The update gate then further blends this result with the previous frame features, and the reconstruction module integrates it with the current frame. To robustly compute attention for noisy features, we propose a residual simplified Swin Transformer with Euclidean distance (RSSTE) in the spatial and temporal denoising modules. Comparative objective and subjective results show that our GRTN achieves denoising performance comparable to SOTA multi-frame delay networks, with only a single-frame delay.

cs.CV↗

arXiv2024

Effects of a Prompt Engineering Intervention on Undergraduate Students' AI Self-Efficacy, AI Knowledge and Prompt Engineering Ability: A Mixed Methods Study

Prompt engineering is critical for effective interaction with large language models (LLMs) such as ChatGPT. However, efforts to teach this skill to students have been limited. This study designed and implemented a prompt engineering intervention, examining its influence on undergraduate students' AI self-efficacy, AI knowledge, and proficiency in creating effective prompts. The intervention involved 27 students who participated in a 100-minute workshop conducted during their history course at a university in Hong Kong. During the workshop, students were introduced to prompt engineering strategies, which they applied to plan the course's final essay task. Multiple data sources were collected, including students' responses to pre- and post-workshop questionnaires, pre- and post-workshop prompt libraries, and written reflections. The study's findings revealed that students demonstrated a higher level of AI self-efficacy, an enhanced understanding of AI concepts, and improved prompt engineering skills because of the intervention. These findings have implications for AI literacy education, as they highlight the importance of prompt engineering training for specific higher education use cases. This is a significant shift from students haphazardly and intuitively learning to engineer prompts. Through prompt engineering education, educators can faciitate students' effective navigation and leverage of LLMs to support their coursework.

cs.CY↗

arXiv2024

Learning on Graphs with Large Language Models(LLMs): A Deep Dive into Model Robustness

Large Language Models (LLMs) have demonstrated remarkable performance across various natural language processing tasks. Recently, several LLMs-based pipelines have been developed to enhance learning on graphs with text attributes, showcasing promising performance. However, graphs are well-known to be susceptible to adversarial attacks and it remains unclear whether LLMs exhibit robustness in learning on graphs. To address this gap, our work aims to explore the potential of LLMs in the context of adversarial attacks on graphs. Specifically, we investigate the robustness against graph structural and textual perturbations in terms of two dimensions: LLMs-as-Enhancers and LLMs-as-Predictors. Through extensive experiments, we find that, compared to shallow models, both LLMs-as-Enhancers and LLMs-as-Predictors offer superior robustness against structural and textual attacks.Based on these findings, we carried out additional analyses to investigate the underlying causes. Furthermore, we have made our benchmark library openly available to facilitate quick and fair evaluations, and to encourage ongoing innovative research in this field.

cs.LG↗

arXiv2024

Investigating Out-of-Distribution Generalization of GNNs: An Architecture Perspective

Graph neural networks (GNNs) have exhibited remarkable performance under the assumption that test data comes from the same distribution of training data. However, in real-world scenarios, this assumption may not always be valid. Consequently, there is a growing focus on exploring the Out-of-Distribution (OOD) problem in the context of graphs. Most existing efforts have primarily concentrated on improving graph OOD generalization from two \textbf{model-agnostic} perspectives: data-driven methods and strategy-based learning. However, there has been limited attention dedicated to investigating the impact of well-known \textbf{GNN model architectures} on graph OOD generalization, which is orthogonal to existing research. In this work, we provide the first comprehensive investigation of OOD generalization on graphs from an architecture perspective, by examining the common building blocks of modern GNNs. Through extensive experiments, we reveal that both the graph self-attention mechanism and the decoupled architecture contribute positively to graph OOD generalization. In contrast, we observe that the linear classification layer tends to compromise graph OOD generalization capability. Furthermore, we provide in-depth theoretical insights and discussions to underpin these discoveries. These insights have empowered us to develop a novel GNN backbone model, DGAT, designed to harness the robust properties of both graph self-attention mechanism and the decoupled architecture. Extensive experimental results demonstrate the effectiveness of our model under graph OOD, exhibiting substantial and consistent enhancements across various training strategies.

cs.LG↗

arXiv2024

Quantum Light Generation based on GaN Microring towards Fully On-chip Source

Integrated quantum light source is increasingly desirable in large-scale quantum information processing.~Despite recent remarkable advances, new material platform is constantly being explored for the fully on-chip integration of quantum light generation, active and passive manipulation, and detection. Here, for the first time, we demonstrate a gallium nitride (GaN) microring based quantum light generation in the telecom C-band, which has potential towards the monolithic integration of quantum light source.~In our demonstration, the GaN microring has a free spectral range of 330 GHz and a near-zero anomalous dispersion region of over 100 nm. The generation of energy-time entangled photon pair is demonstrated with a typical raw two-photon interference visibility of 95.5$\pm$6.5%, which is further configured to generate heralded single photon with a typical heralded second-order auto-correlation $g^{(2)}_{H}(0)$ of 0.045$\pm$0.001. Our results pave the way for developing chip-scale quantum photonic circuit.

quant-ph↗

arXiv2024

Exploring EFL students' prompt engineering in human-AI story writing: an Activity Theory perspective

This study applies Activity Theory to investigate how English as a foreign language (EFL) students prompt generative artificial intelligence (AI) tools during short story writing. Sixty-seven Hong Kong secondary school students created generative-AI tools using open-source language models and wrote short stories with them. The study collected and analyzed the students' generative-AI tools, short stories, and written reflections on their conditions or purposes for prompting. The research identified three main themes regarding the purposes for which students prompt generative-AI tools during short story writing: a lack of awareness of purposes, overcoming writer's block, and developing, expanding, and improving the story. The study also identified common characteristics of students' activity systems, including the sophistication of their generative-AI tools, the quality of their stories, and their school's overall academic achievement level, for their prompting of generative-AI tools for the three purposes during short story writing. The study's findings suggest that teachers should be aware of students' purposes for prompting generative-AI tools to provide tailored instructions and scaffolded guidance. The findings may also help designers provide differentiated instructions for users at various levels of story development when using a generative-AI tool.

cs.CY↗