2024/04/05 by Truman Yuen, Yuen, Truman, Graham A. Watt +3
Business, Management and Accounting · #Artificial Intelligence (cs.AI) #Big Data and Business Intelligence #Computation and Language (cs.CL) #FOS: Computer and information sciences #I.2.7 #I.2.8 #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2404.04351
openalex publication_date 2024/04/05 · openalex created_date 2024/04/10 · openalex updated_date 2026/07/28
Generative Large Language Models enable efficient analytics across knowledge domains, rivalling human experts in information comparisons. However, the applications of LLMs for information comparisons face scalability challenges due to the difficulties in maintaining information across large contexts and overcoming model token limitations. To address these challenges, we developed the novel Abstractive Summarization & Criteria-driven Comparison Endpoint (ASC2End) system to automate information comparison at scale. Our system employs Semantic Text Similarity comparisons for generating evidence-supported analyses. We utilize proven data-handling strategies such as abstractive summarization and retrieval augmented generation to overcome token limitations and retain relevant information during model inference. Prompts were designed using zero-shot strategies to contextualize information for improved model reasoning. We evaluated abstractive summarization using ROUGE scoring and assessed the generated comparison quality using survey responses. Models evaluated on the ASC2End system show desirable results providing insights on the expected performance of the system. ASC2End is a novel system and tool that enables accurate, automated information comparison at scale across knowledge domains, overcoming limitations in context length and retrieval.