Enabling and Analyzing How to Efficiently Extract Information from Hybrid Long Documents with LLMs

• URL: http://arxiv.org/abs/2305.16344v2
• Date: Thu, 7 Mar 2024 13:44:27 GMT
• Title: Enabling and Analyzing How to Efficiently Extract Information from Hybrid Long Documents with LLMs
• Authors: Chongjian Yue, Xinrun Xu, Xiaojun Ma, Lun Du, Hengyu Liu, Zhiming Ding, Yanbing Jiang, Shi Han, Dongmei Zhang
• Abstract summary: This research focuses on harnessing the potential of Large Language Models to comprehend critical information from financial reports. We propose an Automated Financial Information Extraction framework that enhances LLMs' ability to comprehend and extract information from financial reports. Our framework is effectively validated on GPT-3.5 and GPT-4, yielding average accuracy increases of 53.94% and 33.77%, respectively.
• Score: 48.87627426640621
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) demonstrate exceptional performance in textual understanding and tabular reasoning tasks. However, their ability to comprehend and analyze hybrid text, containing textual and tabular data, remains underexplored. In this research, we specialize in harnessing the potential of LLMs to comprehend critical information from financial reports, which are hybrid long-documents. We propose an Automated Financial Information Extraction (AFIE) framework that enhances LLMs' ability to comprehend and extract information from financial reports. To evaluate AFIE, we develop a Financial Reports Numerical Extraction (FINE) dataset and conduct an extensive experimental analysis. Our framework is effectively validated on GPT-3.5 and GPT-4, yielding average accuracy increases of 53.94% and 33.77%, respectively, compared to a naive method. These results suggest that the AFIE framework offers accuracy for automated numerical extraction from complex, hybrid documents.

Related papers

• Scaling Up Summarization: Leveraging Large Language Models for Long Text Extractive Summarization [0.27624021966289597]
  This paper introduces EYEGLAXS, a framework that leverages Large Language Models (LLMs) for extractive summarization. EYEGLAXS focuses on extractive summarization to ensure factual and grammatical integrity. The system sets new performance benchmarks on well-known datasets like PubMed and ArXiv.
  arXiv  Detail & Related papers  (2024-08-28T13:52:19Z)
• AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive Reasoning [93.96463520716759]
  Large language model (LLM) agents have demonstrated impressive capabilities in utilizing external tools and knowledge to boost accuracy and hallucinations. Here, we introduce AvaTaR, a novel and automated framework that optimize an LLM agent to effectively leverage provided tools, improving performance on a given task.
  arXiv  Detail & Related papers  (2024-06-17T04:20:02Z)
• TACT: Advancing Complex Aggregative Reasoning with Information Extraction Tools [51.576974932743596]
  Large Language Models (LLMs) often do not perform well on queries that require the aggregation of information across texts. TACT contains challenging instructions that demand stitching information scattered across one or more texts. We construct this dataset by leveraging an existing dataset of texts and their associated tables. We demonstrate that all contemporary LLMs perform poorly on this dataset, achieving an accuracy below 38%.
  arXiv  Detail & Related papers  (2024-06-05T20:32:56Z)
• Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs [49.57641083688934]
  We introduce a novel approach to anomaly detection in financial data using Large Language Models (LLMs) embeddings. Our experiments demonstrate that LLMs contribute valuable information to anomaly detection as our models outperform the baselines.
  arXiv  Detail & Related papers  (2024-06-05T20:19:09Z)
• Large Language Models as Financial Data Annotators: A Study on Effectiveness and Efficiency [13.561104321425045]
  Large Language Models (LLMs) have demonstrated remarkable performance in data annotation tasks on general domain datasets. We investigate the potential of LLMs as efficient data annotators for extracting relations in financial documents. We demonstrate that the current state-of-the-art LLMs can be sufficient alternatives to non-expert crowdworkers.
  arXiv  Detail & Related papers  (2024-03-26T23:32:52Z)
• FinBen: A Holistic Financial Benchmark for Large Language Models [75.09474986283394]
  FinBen is the first extensive open-source evaluation benchmark, including 36 datasets spanning 24 financial tasks. FinBen offers several key innovations: a broader range of tasks and datasets, the first evaluation of stock trading, novel agent and Retrieval-Augmented Generation (RAG) evaluation, and three novel open-source evaluation datasets for text summarization, question answering, and stock trading.
  arXiv  Detail & Related papers  (2024-02-20T02:16:16Z)
• FinDABench: Benchmarking Financial Data Analysis Ability of Large Language Models [26.99936434072108]
  textttFinDABench is a benchmark designed to evaluate the financial data analysis capabilities of Large Language Models. textttFinDABench aims to provide a measure for in-depth analysis of LLM abilities.
  arXiv  Detail & Related papers  (2024-01-01T15:26:23Z)
• Data-Centric Financial Large Language Models [27.464319154543173]
  Large language models (LLMs) show promise for natural language tasks but struggle when applied directly to complex domains like finance. We propose a data-centric approach to enable LLMs to better handle financial tasks.
  arXiv  Detail & Related papers  (2023-10-07T04:53:31Z)
• LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities [66.36633042421387]
  Large Language Models (LLMs) for Knowledge Graph (KG) construction and reasoning evaluated. We propose AutoKG, a multi-agent-based approach employing LLMs and external sources for KG construction and reasoning.
  arXiv  Detail & Related papers  (2023-05-22T15:56:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.