Papers by Aryan Jain
Too much of product information : Don’t worry, let’s look for evidence! (2023.emnlp-industry)
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| Challenge: | Existing product question answering models do not provide labelled data for the task and description information for products is very lengthy. |
| Approach: | They propose a distant supervision-based NLI model to prepare training data without manual efforts. |
| Outcome: | The proposed model outperforms standard multi-task fine-tuning and improves 6% in human evaluation over baselines. |
CASPER: Bridging Discrete and Continuous Prompt Optimization through Feedback-Guided Gradient Descent (2026.eacl-industry)
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| Challenge: | Existing pipelines for generative tasks require extensive manual effort and domain expertise to achieve task-optimal performance. |
| Approach: | They propose a framework bridging discrete and continuous prompt optimization through feedback-guided gradient descent in embedding space. |
| Outcome: | The proposed framework bridges discrete and continuous prompt optimization through feedback-guided gradient descent in embedding space. |
SQLGenie: A Practical LLM based System for Reliable and Efficient SQL Generation (2025.acl-industry)
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| Challenge: | Large Language Models (LLMs) enable natural language to SQL conversion, but generating accurate, efficient queries is challenging due to ambiguous intent, domain knowledge requirements and database constraints. |
| Approach: | They propose a system for reliable SQL generation that integrates Table Onboarder, SQL Generator and Feedback Augmentation. |
| Outcome: | The proposed system surpasses the best single-LLM baseline by 21.5% and the strongest pipeline competitor by 5.3% on public benchmarks and internal datasets. |
<SYNTACT>: Structuring Your Natural Language SOPs into Tailored Ambiguity-Resolved Code Templates (2025.emnlp-industry)
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Sachin Kumar Giroh, Pushpendu Ghosh, Aryan Jain, Harshal Giridhari Paunikar, Aditi Rastogi, Promod Yenigalla, Anish Nediyanchath
| Challenge: | Unstructured and ambiguous Standard Operating Procedures suffer from ambiguity, missing information, and inconsistency, all of which hinder automation. |
| Approach: | They propose a three-stage LLM framework that transforms unstructured SOPs into a structured plan and an executable code template. |
| Outcome: | The proposed framework shows an 88.4% accuracy and significant reduction in inconsistency on real-world SOPs and synthetic variants. |
PARSE: LLM Driven Schema Optimization for Reliable Entity Extraction (2025.emnlp-industry)
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| Challenge: | Structured information extraction from unstructured text is critical for Software 3.0 systems . current approaches to extract structured information from unstructed text are static contracts . |
| Approach: | They propose a system that automates JSON schemas for LLM consumption and optimizes them for LRM consumption. |
| Outcome: | The proposed system improves extraction accuracy and reduces errors by 92% within the first retry and maintaining practical latency. |