Papers by Subhendu Khatuya
Parameter-Efficient Instruction Tuning of Large Language Models For Extreme Financial Numeral Labelling (2024.naacl-long)
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Subhendu Khatuya, Rajdeep Mukherjee, Akash Ghosh, Manjunath Hegde, Koustuv Dasgupta, Niloy Ganguly, Saptarshi Ghosh, Pawan Goyal
| Challenge: | Existing methods to automatically annotate relevant numerals (GAAP metrics) occurring in financial documents are not cost-effective nor scalable. |
| Approach: | They propose a generative paradigm for annotating GAAP metrics with XBRL tags using metric metadata and a parameter efficient model using LoRA. |
| Outcome: | The proposed model outperforms baseline models on two financial numeric labeling datasets and outperformed several strong baseline models. |
Financial Numeric Extreme Labelling: A dataset and benchmarking (2023.findings-acl)
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Soumya Sharma, Subhendu Khatuya, Manjunath Hegde, Afreen Shaikh, Koustuv Dasgupta, Pawan Goyal, Niloy Ganguly
| Challenge: | In 2019, the SEC mandates that all public companies file periodic financial statements that should contain numerals annotated with a particular label. |
| Approach: | They propose to use a dataset to automate the assignment of a label to a particular numeral span in a sentence from an extremely large label set. |
| Outcome: | The proposed solution outperforms the previous approaches but is less frequent than the pipeline solution. |
Label-semantics Aware Generative Approach for Domain-Agnostic Multilabel Classification (2025.findings-acl)
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| Challenge: | Existing approaches to multi-label text classification are limited by textual data. |
| Approach: | They propose a domain-agnostic generative model framework for multi-label text classification that generates predefined label descriptions and matches them to predefined labels. |
| Outcome: | The proposed model achieves 13.94% and 24.85% performance over all datasets. |
Program of Thoughts for Financial Reasoning: Leveraging Dynamic In-Context Examples and Generative Retrieval (2025.emnlp-main)
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| Challenge: | Numerical reasoning remains a challenging area for large language models (LLMs). |
| Approach: | They propose a two-step framework to enhance LLM's capabilities in financial numerical reasoning by using a generative retriever and context-aware program of thought prompting. |
| Outcome: | The proposed model surpasses previous benchmarks with execution accuracy improvements of 5.98% and 4.05%, respectively. |