6 papers
Sustainable Hybrid Document-Routed Retrieval for Financial RAG: Resolving the Robustness-Precision Trade-off
Zhiyuan Cheng, Longying Lai, Yue Liu
Retrieval-Augmented Generation (RAG) systems for financial document QA typically follow a chunk-based paradigm: documents are split into fragments, embedded, and retrieved by simil…
Energy-Efficient On-Device RAG on a Mobile NPU: System Design and Benchmark on Snapdragon X Elite
Zhiyuan Cheng, Longying Lai
Retrieval-Augmented Generation (RAG) pipelines are compute-intensive, combining embedding, retrieval, reranking, and large language model (LLM) generation. Running them entirely on…
The Efficiency Gap in Byte Modeling
Celine Lee, Jing Nathan Yan, Chen Liang +9
Modern language models have historically relied on two dominant design choices: subword tokenization and autoregressive (AR) ordering. These design decisions bake in priors that di…
Enhancing Financial Report Question-Answering: A Retrieval-Augmented Generation System with Reranking Analysis
Zhiyuan Cheng, Longying Lai, Yue Liu +2
Financial analysts face significant challenges extracting information from lengthy 10-K reports, which often exceed 100 pages. This paper presents a Retrieval-Augmented Generation…
Improving the Completeness and Comparability of Segment Disclosures: A Large Language Model Approach
Yue Liu, Zhiyuan Cheng, Longying Lai
Segment-level disclosures are a central component of financial reporting, providing insight into firms' internal organization and the allocation of economic activities across opera…
AutoNeural: Co-Designing Vision-Language Models for NPU Inference
Wei Chen, Liangmin Wu, Yunhai Hu +9
While Neural Processing Units (NPUs) offer high theoretical efficiency for edge AI, state-of-the-art Vision--Language Models (VLMs) tailored for GPUs often falter on these substrat…