most citedResearch on the Application of Deep Learning-based BERT Model in Sentiment Analysis

6 citations · 15 across the 11 of their papers we have counts for

collaborators

11 papers

cs.IR2026

Architecture Matters More Than Scale: A Comparative Study of Retrieval and Memory Augmentation for Financial QA Under SME Compute Constraints

Jianan Liu, Jing Yang, Xianyou Li +4

The rapid adoption of artificial intelligence (AI) and large language models (LLMs) is transforming financial analytics by enabling natural language interfaces for reporting, decis…

cs.MA2026

Recursive Multi-Agent Trading System: Iterative Optimized Portfolio Strategy Under Geopolitical Uncertainty

Jing Yang, Yichao Wu, Jianan Liu +4

Recursive Multi-Agent Trading System (RMATS) integrates four specialized agents -- Sentiment, Report, Analysis, and Risk -- coordinated through a recursive Manager Agent with itera…

cs.IR2026

TA-Mem: Tool-Augmented Autonomous Memory Retrieval for LLM in Long-Term Conversational QA

Mengwei Yuan, Jianan Liu, Jing Yang +4

Large Language Model (LLM) has exhibited strong reasoning ability in text-based contexts across various domains, yet the limitation of context window poses challenges for the model…

cs.IR2026

Tiny-Critic RAG: Empowering Agentic Fallback with Parameter-Efficient Small Language Models

Yichao Wu, Penghao Liang, Yafei Xiang +5

Retrieval-Augmented Generation (RAG) grounds Large Language Models (LLMs) to mitigate factual hallucinations. Recent paradigms shift from static pipelines to Modular and Agentic RA…

cs.CL2026

DynaRAG: Bridging Static and Dynamic Knowledge in Retrieval-Augmented Generation

Penghao Liang, Mengwei Yuan, Jianan Liu +4

We present DynaRAG, a retrieval-augmented generation (RAG) framework designed to handle both static and time-sensitive information needs through dynamic knowledge integration. Unli…

cs.SE2024★ 3 cited

Automating the Training and Deployment of Models in MLOps by Integrating Systems with Machine Learning

Penghao Liang, Bo Song, Xiaoan Zhan +2

This article introduces the importance of machine learning in real-world applications and explores the rise of MLOps (Machine Learning Operations) and its importance for solving ch…

cs.RO2024★ 1 cited

Automated Lane Change Behavior Prediction and Environmental Perception Based on SLAM Technology

Han Lei, Baoming Wang, Zuwei Shui +2

In addition to environmental perception sensors such as cameras, radars, etc. in the automatic driving system, the external environment of the vehicle is perceived, in fact, there…

cs.IR2024★ 2 cited

Maximizing User Experience with LLMOps-Driven Personalized Recommendation Systems

Chenxi Shi, Penghao Liang, Yichao Wu +2

The integration of LLMOps into personalized recommendation systems marks a significant advancement in managing LLM-driven applications. This innovation presents both opportunities…

cs.CL2024★ 6 cited

Research on the Application of Deep Learning-based BERT Model in Sentiment Analysis

Yichao Wu, Zhengyu Jin, Chenxi Shi +2

This paper explores the application of deep learning techniques, particularly focusing on BERT models, in sentiment analysis. It begins by introducing the fundamental concept of se…

cs.AI2024

Emerging Synergies Between Large Language Models and Machine Learning in Ecommerce Recommendations

Xiaonan Xu, Yichao Wu, Penghao Liang +2

With the boom of e-commerce and web applications, recommender systems have become an important part of our daily lives, providing personalized recommendations based on the user's p…

cs.LG2024★ 3 cited

LoRA-SP: Streamlined Partial Parameter Adaptation for Resource-Efficient Fine-Tuning of Large Language Models

Yichao Wu, Yafei Xiang, Shuning Huo +2

In addressing the computational and memory demands of fine-tuning Large Language Models(LLMs), we propose LoRA-SP(Streamlined Partial Parameter Adaptation), a novel approach utiliz…