Parameter Efficient Instruction Tuning of LLMs for Financial Applications

摘要

XBRL tagging in financial texts involves categorizing entities into numerous labels, presenting challenges for state-of-the-art models. Financial reports like 10-Q and 10-K, which must be tagged with XBRL according to a taxonomy with thousands of labels. The FNXL dataset exemplifies this with 2,794 labels. Manual tagging is neither scalable nor cost-effective, necessitating automatic annotation methods. Additionally, summarizing long Earnings Call Transcripts (ECTs) is crucial for financial decision-making. The ECTSum dataset highlights challenges in automatic summarization, including a high compression ratio and documents exceeding typical LLM token limits. This study proposes novel methods for both XBRL tagging and ECT summarization.