{"type":"ScholarlyArticle","title":"Parameter Efficient Instruction Tuning of LLMs for Financial Applications","authors":[{"name":"Subhendu Khatuya","slug":"subhendu-khatuya","given":"Subhendu","family":"Khatuya","affiliation":"Computer Science and Engineering Department , IIT Kharagpur"}],"abstract":"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.","doi":"10.24963/ijcai.2024/962","url":"https://ijcai.org/proceedings/2024/962","pdf":"https://ijcai.org/proceedings/2024/0962.pdf","markdown":"https://ijcai.org/proceedings/2024/962.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/962","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":962,"pages":{"first":8494,"last":8495},"keywords":["DC: Machine Learning","DC: Natural Language Processing","DC: Multidisciplinary Topics and Applications","DC: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Khatuya et al., 2024] Subhendu Khatuya, R Mukherjee, A Ghosh, M Hegde, K Dasgupta, N Ganguly, S Ghosh, P Goyal. Parameter-Efficient Instruction Tuning of Large Lan- guage Models For Extreme Financial Numeral Labelling. In Proceedings of North American Chapter of the Association for Computational Linguistics (NAACL), Main Conference, 2024 [","title":"Parameter-Efficient Instruction Tuning of Large Language Models For Extreme Financial Numeral Labelling","authors":"Subhendu Khatuya, Mukherjee, M Ghosh, K Hegde, N Dasgupta, S Ganguly, P Ghosh, Goyal","year":"2024","venue":"Proceedings of North American Chapter of the Association for Computational Linguistics (NAACL), Main Conference","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Khatuya et al., 2024] Subhendu Khatuya, K Sinha, S Ghosh, N Ganguly and P Goyal. Instruction-Guided Bullet Point Summarization of Long Financial Earnings Call Transcripts. SIGIR Conference on Research and Development in Infor- mation Retrieval (SIGIR), 2024","title":"Instruction-Guided Bullet Point Summarization of Long Financial Earnings Call Transcripts","authors":"Subhendu Khatuya, S Sinha, N Ghosh, P Ganguly, Goyal","year":"2024","venue":"SIGIR Conference on Research and Development in Information Retrieval (SIGIR)","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Khatuya et al., 2023] S Sharma, Subhendu Khatuya, M Hegde, A Shaikh, K Dasgupta, P Goyal, and N Ganguly. Fi- nancial numeric extreme labelling: dataset and benchmark- ing. ACL 2023 (Findings)","title":"Financial numeric extreme labelling: dataset and benchmarking","authors":"Subhendu Sharma, M Khatuya, Hegde, K Shaikh, P Dasgupta, Goyal, Ganguly","year":null,"venue":"ACL 2023","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}