arXiv subjects

Rongyu Lin

Publications and source records attributed to Rongyu Lin.

5 recordsLinked to original sources

arXiv2025

LLM-Based Multi-Agent System for Simulating and Analyzing Marketing and Consumer Behavior

Simulating consumer decision-making is vital for designing and evaluating marketing strategies before costly real-world deployment. However, post-event analyses and rule-based agent-based models (ABMs) struggle to capture the complexity of human behavior and social interaction. We introduce an LLM-powered multi-agent simulation framework that models consumer decisions and social dynamics. Building on recent advances in large language model simulation in a sandbox environment, our framework enables generative agents to interact, express internal reasoning, form habits, and make purchasing decisions without predefined rules. In a price-discount marketing scenario, the system delivers actionable strategy-testing outcomes and reveals emergent social patterns beyond the reach of conventional methods. This approach offers marketers a scalable, low-risk tool for pre-implementation testing, reducing reliance on time-intensive post-event evaluations and lowering the risk of underperforming campaigns.

cs.AI↗

arXiv2024

Abstract2Appendix: Academic Reviews Enhance LLM Long-Context Capabilities

Large language models (LLMs) have shown remarkable performance across various tasks, yet their ability to handle long-context reading remains challenging. This study explores the effectiveness of leveraging high-quality academic peer review data for fine-tuning LLMs to enhance their long-context capabilities. We compare the Direct Preference Optimization (DPO) method with the Supervised Fine-Tuning (SFT) method, demonstrating DPO's superiority and data efficiency. Our experiments show that the fine-tuned model achieves a 4.04-point improvement over phi-3 and a 2.6\% increase on the Qasper benchmark using only 2000 samples. Despite facing limitations in data scale and processing costs, this study underscores the potential of DPO and high-quality data in advancing LLM performance. Additionally, the zero-shot benchmark results indicate that aggregated high-quality human reviews are overwhelmingly preferred over LLM-generated responses, even for the most capable models like GPT-4o. This suggests that high-quality human reviews are extremely rich in information, reasoning, and long-context retrieval, capabilities that even the most advanced models have not fully captured. These findings highlight the high utility of leveraging human reviews to further advance the field.

cs.CL↗

arXiv2024

Multi-modal Preference Alignment Remedies Degradation of Visual Instruction Tuning on Language Models

Multi-modal large language models (MLLMs) are expected to support multi-turn queries of interchanging image and text modalities in production. However, the current MLLMs trained with visual-question-answering (VQA) datasets could suffer from degradation, as VQA datasets lack the diversity and complexity of the original text instruction datasets with which the underlying language model was trained. To address this degradation, we first collect a lightweight, 5k-sample VQA preference dataset where answers were annotated by Gemini for five quality metrics in a granular fashion and investigate standard Supervised Fine-tuning, rejection sampling, Direct Preference Optimization (DPO) and SteerLM algorithms. Our findings indicate that with DPO, we can surpass the instruction-following capabilities of the language model, achieving a 6.73 score on MT-Bench, compared to Vicuna's 6.57 and LLaVA's 5.99. This enhancement in textual instruction-following capability correlates with boosted visual instruction performance (+4.9\% on MM-Vet, +6\% on LLaVA-Bench), with minimal alignment tax on visual knowledge benchmarks compared to the previous RLHF approach. In conclusion, we propose a distillation-based multi-modal alignment model with fine-grained annotations on a small dataset that restores and boosts MLLM's language capability after visual instruction tuning.

cs.CL↗

arXiv2020

BAlN alloy for enhanced two-dimensional electron gas characteristics of GaN-based high electron mobility transistor

The emerging wide bandgap BAlN alloys have potentials for improved III-nitride power devices including high electron mobility transistor (HEMT). Yet few relevant studies have been carried. In this work, we have investigated the use of the B0.14Al0.86N alloy as part or entirety of the interlayer between the GaN buffer and the AlGaN barrier in the conventional GaN-based high electron mobility transistor (HEMT). The numerical results show considerable improvement of the two-dimensional electron gas (2DEG) concentration with small 2DEG leakage into the ternary layer by replacing the conventional AlN interlayer by either the B0.14Al0.86N interlayer or the B0.14Al0.86N/AlN hybrid interlayer. Consequently, the transfer characteristics can be improved. The saturation current can be enhanced as well. For instance, the saturation currents for HEMTs with the 0.5 nm B0.14Al0.86N/0.5 nm AlN hybrid interlayer and the 1 nm B0.14Al0.86N interlayer are 5.8% and 2.2% higher than that for the AlN interlayer when VGS-Vth= +3 V.

physics.app-ph↗

arXiv2020

BAlN for III-nitride UV light emitting diodes: undoped electron blocking layer

The undoped BAlN electron-blocking layer (EBL) is investigated to replace the conventional AlGaN EBL in light-emitting diodes (LEDs). Numerical studies of the impact of variously doped EBLs on the output characteristics of LEDs demonstrate that the LED performance shows heavy dependence on the p-doping level in the case of the AlGaN EBL, while it shows less dependence on the p-doping level for the BAlN EBL. As a result, we propose an undoped BAlN EBL for LEDs to avoid the p-doping issues, which a major technical challenge in the AlGaN EBL. Without doping, the proposed BAlN EBL structure still possesses a superior capacity in blocking electrons and improving hole injection compared with the AlGaN EBL having high doping. This study provides a feasible route to addressing electron leakage and insufficient hole injection issues when designing UV LED structures.

physics.app-ph↗