Papers by Promod Yenigalla
MARCO: Multi-Agent Real-time Chat Orchestration (2024.emnlp-industry)
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Anubhav Shrimal, Stanley Kanagaraj, Kriti Biswas, Swarnalatha Raghuraman, Anish Nediyanchath, Yi Zhang, Promod Yenigalla
| Challenge: | MARCO is a multi-agent real-time chat orchestration framework for automating workflows that require interactions with tools, reasoning, and human collaboration. |
| Approach: | They propose a multi-agent real-time chat orchestration framework for automating workflows using LLMs. |
| Outcome: | The proposed framework performs with 94.48% accuracy and 92.74% accuracy on restaurant and retail conversations datasets and 44.91% improved latency and 33.71% cost reduction in a production setting. |
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. |
NER-MQMRC: Formulating Named Entity Recognition as Multi Question Machine Reading Comprehension (2022.naacl-industry)
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| Challenge: | Named Entity Recognition (NER) is a task of locating and classifying entities mentioned in unstructured text into predefined categories. |
| Approach: | They propose to use a BERT-based multi-question MRC task where multiple questions (one question per entity) are considered at the same time for a single text. |
| Outcome: | The proposed architecture leads to 2.5 times faster training and 2.3 times faster inference on three NER datasets. |
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. |
I-SEE: An Instruction-tuned, SOP-Enhanced Quality Evaluator for Product Content (2025.emnlp-industry)
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| Challenge: | Existing approaches to content evaluation treat information uniformly without prioritizing based on customer relevance. |
| Approach: | They propose a framework that combines domain expertise with a single instruction to improve content. |
| Outcome: | a new framework outperforms existing models in detecting inconsistencies across 20 product categories and 150 product specific features. |
<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. |
AMUSED: A Multi-Stream Vector Representation Method for Use in Natural Dialogue (2020.lrec-1)
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| Challenge: | Current architectures only take care of semantic and contextual information for a given query and fail to fully account for syntactic and external knowledge which are crucial for generating responses in a chit-chat system. |
| Approach: | They propose a multi-stream deep learning architecture that learns unified embeddings for query-response pairs by incorporating Graph Convolution Networks over their dependency parse. |
| Outcome: | The proposed architecture improves on the next sentence prediction task and significantly improves existing techniques. |
Weakly supervised hierarchical multi-task classification of customer questions (2023.acl-industry)
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Jitenkumar Rana, Promod Yenigalla, Chetan Aggarwal, Sandeep Sricharan Mukku, Manan Soni, Rashmi Patange
| Challenge: | Identifying granular and actionable topics from customer questions helps improve the overall customer experience. |
| Approach: | They propose a weakly supervised Hierarchical Multi-task Classification Framework to identify granular topics from customer questions . a clustering based taxonomy creation and data labeling module is used to create taxonomies and labelled data with minimal supervision. |
| Outcome: | The proposed model achieves 13% better accuracy over single-task classification frameworks . it can adapt to constantly evolving taxonomy without need of re-training . |
MARS: Multilingual Aspect-centric Review Summarisation (2024.emnlp-industry)
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| Challenge: | Existing methods for summarizing customer feedback are not able to extract actionable reviews into a specific target language. |
| Approach: | They propose a framework involving extract-then-summarise to summariser customer feedback into a specific language. |
| Outcome: | The proposed framework improves abstractive baselines and efficiency to real-time systems. |
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. |
InsightNet : Structured Insight Mining from Customer Feedback (2023.emnlp-industry)
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Sandeep Sricharan Mukku, Manan Soni, Chetan Aggarwal, Jitenkumar Rana, Promod Yenigalla, Rashmi Patange, Shyam Mohan
| Challenge: | Existing methods for extracting structured insights from reviews suffer from drawbacks . lack of structure, non-standard aspect names, lack of abundant training data limit their effectiveness and applicability. |
| Approach: | They propose a semi-supervised multi-level taxonomy from raw customer reviews and a semantic similarity heuristic approach to generate labelled data. |
| Outcome: | The proposed approach outperforms existing methods in structure, hierarchy and completeness. |
PROBES : Performance and Relevance Observation for BEtter Search (2026.eacl-industry)
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| Challenge: | Qualitative search is essential for the success of online platforms, authors say . large-scale evaluation of search systems is essential to ensure high-quality user experiences . |
| Approach: | They propose a multi-task system powered by Large Language Models for end-to-end evaluation of semantic search systems. |
| Outcome: | The proposed system provides more precise and consistent relevance assessments across query categories. |