Summary of Parameter-efficient Instruction Tuning Of Large Language Models For Extreme Financial Numeral Labelling, by Subhendu Khatuya et al.
Parameter-Efficient Instruction Tuning of Large Language Models For Extreme Financial Numeral Labelling
by Subhendu Khatuya, Rajdeep Mukherjee, Akash Ghosh, Manjunath Hegde, Koustuv Dasgupta, Niloy Ganguly, Saptarshi Ghosh, Pawan Goyal
First submitted to arxiv on: 3 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract proposes a novel approach to annotating financial documents by leveraging Large Language Models (LLMs) and metric metadata information. The goal is to automatically label numerals occurring in these documents with their corresponding XBRL tags, which is an extreme classification problem. The authors investigate the feasibility of solving this problem using instruction tuning of LLMs and propose a parameter-efficient solution called LoRA. Experimental results on two recently released datasets show that the proposed model, FLAN-FinXC, achieves state-of-the-art performances, outperforming several strong baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to automatically label financial documents by using large language models. The goal is to help computers understand what numbers in these documents mean. To do this, they use information about the numbers and their meanings to train a special type of AI model. This allows them to correctly identify the labels for most of the numbers in the documents. |
Keywords
» Artificial intelligence » Classification » Instruction tuning » Lora » Parameter efficient
