Assisting humans in complex comparisons: automated information comparison at scale

arXiv:2404.04351v1 Announce Type: cross
Abstract: 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 (ASC$^2$End) system to automate information comparison at scale. Our system employs Semantic Text Similarity comparisons for generating evidence-supported analyses. …

abstract analytics applications arxiv automated challenges comparison cs.ai cs.cl cs.lg domains experts face generative however human humans information knowledge language language models large language large language models limitations llms scalability scale token type