Papers by Shashishekar Ramakrishna
Synthesizing question answering data from financial documents: An End-to-End Multi-Agent Approach (2026.eacl-industry)
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| Challenge: | Large language models excel at financial reasoning but their deployment for enterprise use cases remains costly and often constrained by latency, privacy, and regulatory requirements. |
| Approach: | They propose a pipeline that extracts and selects relevant content from unstructured financial documents and generates QA pairs from the selected content for SLM fine-tuning. |
| Outcome: | The proposed model outperforms models trained on previous manual models and achieves competitive in-distribution performance. |
Fine-tuning Smaller Language Models for Question Answering over Financial Documents (2024.findings-emnlp)
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| Challenge: | Recent research has shown that smaller language models can acquire substantial reasoning abilities when fine-tuned with reasoning exemplars crafted by a significantly larger teacher model. |
| Approach: | They propose to fine-tune several smaller model to generate programs that encode the required financial reasoning and calculations. |
| Outcome: | The proposed model outperforms the teacher model in the financial domain by adjusting the entity extraction for the specific data format. |